Memoriasciima2013

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resumen del congreso internacional de ingeniería mecatronica

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Memoriasciima2013

  1. 1. 2013 II International Congress of Engineering Mechatronics and Automation (CIIMA) 23 al 25 de octubre del 2013 Universidad de La Salle- Bogotá Colombia
  2. 2. Copyright 2013 II International Congress of Engineering Mechatronics and Automation (CIIMA) Copyright ©, 2013, by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Copyright and Reprint Permission Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law, for private use of patrons, those articles in this volume that carry a code at the bottom of the first provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. Other copying, reprint or reproduction request should be addresses to IEEE Copyrights Manager, IEEE Service Center, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331. IEEE Catalog Number CFP1385W-CDR ISBN 978-1-4799-2470-7 Additional copies of this publication are available from Curran Associates, Inc. 57Morehouse Lane Red Hook, NY 12751 USA + 1 845 758 0400 +1 845 758 2633 (FAX) Email: curran@proceedings.com
  3. 3. CONFERENCIAS 1. Diagnosis from Chronicles: an overview of related challenges Audine Subias CNRS; LAAS; 7 avenue du colonel Roche F-31400 Toulouse, France Univ of Toulouse, INSA, LAAS, F-31400 Toulouse, France subias@laas.fr 2. Integration of Different Facets of Diagnosis from Control and AI L. Travé-Massuyés CNRS, LAAS, 7, avenue du Colonel Roche, F-31400 Toulouse, France Univ of Toulouse, LAAS, F-31400 Toulouse, France Email: louise@laas.fr PONENCIAS 1. Deducción y Validación de un Modelo Dinámico de la Transferencia Térmica en un Invernadero a Escala Oscar Alexánder Bellón Hernández Facultad de Ciencias e Ingeniería Universidad de Boyacá Tunja, Colombia 2. Control Estadístico Aplicado a la Detección de Síntomas de Sucesos Operacionales en Producción de Crudo con Sistemas de Levantamiento Artificial BES . Cesar Pereira, Jorge Prada. Ecopetrol S.A. Piedecuesta, Colombia 3. A comparative study of geometric path planning methods for a mobile robot: Potential field and Voronoi diagrams Edwar Jacinto Gómez, Fernando Martínez Santa, Fredy Hernán Martínez Sarmiento. Distrital University Francisco José de Caldas 4. A comparative analysis of adaptive visual servo control for Robots Manipulators in 2D Maximiliano Bueno López, Daniel Mariño Lizarazo. Ingeniería Eléctrica- Ingeniería en automatización- Universidad de La Salle. 5. Integración de redes de sensores inalámbricos (WSN) IEEE 802.15.4 – 802.11 para automatización industrial Álvaro Romero, Alejandro Marín, Julián Orozco, y Jovani Jiménez. Universidad Nacional de Colombia
  4. 4. 6. Filtro digital ajustable usando micro softcore en FPGA para frecuencias entre 200 hz y 20 khz Jorge E. Reita, Juan C. Uribe, Edwar Jacinto, Fernando Martínez Universidad Distrital Francisco José de Caldas, 7. Sistema Telemétrico De Registro De Señales De Emg Superficiales Basado En Tecnología Bluetooth Robín Alfonzo Blanco, Brian Chacón Hernández, Leonardo Andrés Góngora Velandia. 8. Diseño De Un Equipo De Soldadura Basado En Gas HHO Extraído Del Agua Gustavo Adolfo Ramírez Piedrahita. Facultad de minas. Universidad Nacional de Colombia 9. Control GPI Multivariable de un Exoesqueleto para Asistencia de Marcha en Personas con Discapacidad Motora J. Arcos, A. Tovar, J. Cortés, H. Díaz, L. Sarmiento. Universidad de San Buenaventura, Bogotá; Indiana University Purdue University Indianapolis; Universidad Nacional de Colombia, Bogotá. 10. Detección y Seguimiento Facial en Niños Autistas con Bajo Nivel de Funcionamiento Y. Castro, J.C. Bejarano, J. D. Posada, J.A.Villanueva. Department of Engineering, Universidad Autónoma Del Caribe –Barranquilla, Colombia. 11. Modelo Para La Apliacion De La Norma Iec61131-3 En Un Sistema De Manufactura Flexible Oscar Mauricio Arévalo Rodríguez, Álvaro Antonio Patiño Forero Universidad de La Salle 12. Diseño Scada de una autoclave, aplicando la norma IEC 61131-3. Germán Alejandro Piñeros Bernal, Álvaro Patiño Universidad de La Salle 13. Diseño y manufactura de un robot pendular suspendido de 4 gdl, para utilizarlo dentro de un aplicativo de realidad virtual aumentada con colecciones de museografia y telecontrol por internet Méndez M. Luis Miguel, Uribe M. Bernardo y Pantoja R. Cesar Augusto. Department of Mechanical and MechatronicsEngineering Universidad Nacional de Colombia 14. Evaluación del Desempeño de un Controlador MPC para Una Planta Multivariable de Tanques Interactuantes Mario A. Zuñiga M., Pedro L. Rivera W., Francisco Franco Departamento de Electrónica y Telecomunicaciones, Ingeniería en Automática Industrial, Universidad del Cauca
  5. 5. Popayán, Colombia 15. Diseño E Implementación De Una Máquina Para La Producción De Papas Chips Leonardo Alberto Ciendua, Jonathan Jair Díaz, Luis Fernando Morales, Chiara Seidel Schlenker, Álvaro A. Patiño-Forero, Jairo O. Montoya Programa de Ingeniería en Automatización, Universidad de la Salle Bogotá, Colombia 16. Tecnificación de equipos de control y monitorización de material particulado para mejorar la calidad del aire en zonas de explotación y coquización de carbón en Boyacá Oscar AlexánderBellón Hernández, Dora Marcela Benítez Ramírez Facultad de Ciencias e Ingeniería Universidad de Boyacá 17. Modeling and tracking control of a pneumatic servo positioning system Iván Ramírez 18. Afinador de Guitarra Acústica Semiautomático Juan Bejarano, Leandro Torres, Cesar Zúñiga, Facultad de Ingeniería, Universidad Autónoma del Caribe 19. Analysis of alarm management in startups and shutdowns for oil refining processes Vásquez John, Prada Jorge, Agudelo Carlos, Jiménez José.Escuela Colombiana de Carreras Industriales ECCI Engineering department, Andes University Automation Group Instituto Colombiano del Petróleo ICP 20. Optimización Multi-objetivo de un Controlador PID Aplicando Algoritmos Bioinspirados Juan Camilo Castro Pinto, María Alejandra Guzmán Pardo 21. B-WalkMóvil -Sistema de Información Móvil para la Ubicación de Personas Invidentes Diana Lancheros Cuesta, Laura Cardozo, Laura Corredor, Ingeniería en Automatización, Universidad de La Salle 22. Diseño y Construcción de un Prototipo de Máquina de Rehabilitación de Mano y Muñeca Camilo Andrés Cáceres Flórez, Jefry Anderson Mora Montañez, Robinson Jiménez Moreno GAV- Universidad Militar Nueva Granada 23. Comparación de enfoques de sistemas de control tradicionales y el paradigma de los Sistemas Holónicos de Manufactura
  6. 6. Luis A Cruz Salazar, Oscar A Rojas Alvarado. Universidad Antonio Nariño, Universidad del Cauca 24. Diseño Y Construcción De Un Rov Sumergible Nelson O. Rodríguez Quiroz, Jairo O. Montoya G. Universidad De La Salle - Programa de Ingeniería en Automatización 25. Implementación de redes neuronales y lógica difusa para la clasificación de patrones obtenidos por un Sónar Muñoz Aldana, David, Cruz Salazar, Luis A., Contreras Montes, Juan Fundación Universitaria Tecnológico Comfenalco Escuela Naval Almirante Padilla “ENAP” 26. Control de Temperatura en un Bioproceso Utilizando Lógica Difusa Andrea Santos Morales, Cristian Camilo Beltrán Hernández, Claudia L. Garzón-Castro Grupo de Investigación CAPSAB- Facultad de Ingeniería - Universidad de La Sabana - Chía, Colombia 27. Comparative analysis of non-linear filters for attitude estimation in a low-cost inertial station Sebastián López R., Julian,Munoz. Ruiz Fredy,Cheguini Mazeyar 28. Diseño de un Prototipo de Planta Para el Control de Nivel Marco Tulio Calderón Acuña, Luis Hildebrando Alzate, José Ariel Gil García Departamento de Física, Universidad de Caldas, Manizales, Colombia 29. Aproximacion Al Diseño De Los Eslabones De Un Robot Delta Lucas Urrea Mantilla, Sergio Alejandro Medina, Ricardo Andrés Castillo, Oscar Fernando Avilés Programa de ingeniería en Mecatrónica, Universidad Militar Nueva Granada, Bogotá, Colombia. 30. Diseño e implementación de un Prototipo de Torno Fresador de Control Numérico Computarizado Fausto Acuña Departamento de Energía y Mecánica- ESPE, Latacunga, Ecuador Andrés Gordón, Walter Núñez - Carrera de Ingeniería Mecatrónica- ESPE Latacunga, Ecuador 31. Análisis Comparativo De Técnicas De Control Convencional E Inteligente Con Los Sistemas De Articulación Flexible Y Bola Biga Germán E. Polanco Aristizábal, Oscar E. Soto Castañeda, Jesús A. López Departamento de Automática y electrónica, Universidad Autónoma de occidente.
  7. 7. 33. Diseño de Control Neuronal por PLC para una Planta de Laboratorio Mario A. Fernández F., Universidad de Talca, Curicó, Chile William Gutiérrez M., SENA-Regional Valle, Cali, Colombia Jesús A. López S., Universidad Autónoma de Occidente, Cali, Colombia 32. Inteligencia De Enjambres Aplicada Al Control Adaptativo Navas, Andrés Felipe., López, Jesús A. Universidad Autónoma de Occidente – Cali 33. Implementación de una Red Neuronal Artificial tipo SOM en una FPGA para la resolución de trayectorias tipo laberinto Callejas Iván, Piñeros Juan, Rocha Juan, Hernández Ferney, Delgado Fabio Ingeniería Electrónica, Universidad INCCA de Colombia Bogotá, Colombia 34. Reconocimiento de Embarcaciones Marinas Usando Redes Neuronales Esmeide Leal, Nallig Leal, Ronald Messino, Richard Aroca. 35. Cálculo de camino óptimo para manipulador articulado SCARA sujeto a obstáculos. Carlos G. Pillajo Departamento de Control y Automatización Universidad Politécnica Salesiana Quito, Ecuador 36. Estimación de orientación de herramienta y tuerca utilizando la visión del robot NAO Carlos Peña, José Hoyos, Flavio Prieto Jorge Ayala, Claudia Garzón-Castro Facultad de ingeniería Universidad Nacional de Colombia, Universidad de La Sabana 37. Diseño de un Sistema de Maniobra y Pateo la Bola, para un Robot Categoría SSL para la RobocuP Rangel Díaz, Jorge Eliécer, Sanabria Torres, Jairo Andrés Universidad de La Salle; Trane de Colombia SA 38. Técnicas De Control Adaptativo Aplicadas A Un Mezclador Por Baches Con Agitación Continua Juan Esteban Betancur, David Velásquez Rendón, José Fernando Osorio Brand, Rigoberto Maldonado. Escuela de Ingeniería de Antioquia. Envigado 39. Design and implementation of a prototype orthoses for prevention and treatment of Carpal Tunnel Syndrome (CTS)
  8. 8. Karen Edilma Garzón Cruz, José Luis Rubiano Fernández Universidad de la Salle, Bogotá 40. Design of an under actuated Altering tetrapod Robot bio Inspired on Scorpions Jorge I. Montalvo N. Universidad Autónoma de Occidente POSTER 1. Sistema automatizado para la caracterización de la calidad espacial de un haz láser Norma Alicia Barboza Tello, Eduardo Antonio Murillo Bracamontes, José Luis Rodríguez Verduzco 2. Control Basado en PLC de un Brazo Robótico para el Transporte y Almacenamiento de Productos en una Celda de manufactura Jurado Muñoz Sandra M., Cubillos Rojas Jean A., Muñoz Magín Elviz Jhony, Muñoz Tafur Johan Fabián. Institución Universitaria Tecnológica de Comfacauca Unicomfacauca Facultad de Ingeniería – Tecnología en Electrónica- Popayán, Colombia 3. Desarrollo De Una Herramienta Extrusora De Polímero Utilizada En Una Impresora 3d Fdm Edgar A. Torres, Jersson X. León, Edwin Torres. Escuela de Ingeniería Electromecánica, Grupo de Energías y Nuevas Tecnologías-GENTE Universidad Pedagógica y Tecnológica de Colombia, Duitama, Colombia 4. Sistema Domótico Para Discapacitados Controlado Por Voz Iván Santiago García Peñaloza, Luis Eduardo Sierra Catillo, Adriana Patricia Arias Díaz, Edwin Ferney Bonilla Torres, Salvador Pacheco. División de Ingenierías y Arquitectura, Facultad de Ingeniería Mecatrónica, Universidad Santo Tomás- Bucaramanga, Colombia. 5. Control Adaptativo en una junta P-R (del tipo Prismático Rotacional) Gamba, Nicolás. Sierra, Nelson y Romero, David. Universidad Nacional de Colombia 6. Modelo Para La Implementación De La Norma Iec 61131-3 En Un Sistema Integrado De Manufactura Julio Alberto Ambrosio, Erick Stiven Ariza, Julián David Guaqueta, Álvaro Antonio Patiño. Universidad de la Salle, AVARC/SAVARC Bogotá D.C, Colombia. 7. Plataforma didáctica para el estudio de procesos térmicos en laboratorio de Instrumentación industrial
  9. 9. A. Chacón García, H. Montaña Quintero, Departamento de Ingeniería Electrónica Pontificia Universidad Javeriana, Bogotá, Colombia Tecnología en Electrónica Universidad Distrital Francisco José de Caldas 8. Actuador Hidráulico para Prótesis de Rodilla Edilberto Mejía Ruda, Sebastián Jiménez Gómez, Oscar Fernando Avilés Sánchez, Oscar Iván Caldas Flautero, Juan Camilo Hernández. Mejía, Programa de Ingeniería Mecatrónica –Universidad Militar Nueva Granada 9. Analysis of Kinematics and Dynamics for ABB IRB-140 Serial Robots and Evaluation of Energy Consumption in the Tracking of a Path. Mauro Baquero Suarez, Ricardo Ramírez Heredia, Mechatronics Engineering Department, Universidad Nacional 10. Control De Plataforma De Stewart Mediante Procesamiento De Imagen Robinson Jiménez Moreno, Oscar Fernando Avilés S. y Jorge Riveros, GAV - Universidad Militar Nueva Granada 11. Diseño de un Sistema Dosificador y Mezclador de Concentrados H. González, H. González, J. Bohórquez, J. Quintero, J. Gómez Grupo de Investigación de Control & Mecatrónica - UNAB 12. Diseño De Controladores Pid Para Sistemas De Segundo Orden Usando LoopShaping Robusto Mario F. Jiménez, Andrés A. Ramírez. Departamento de Ingeniería Mecatrónica - Fundación Universitaria Agraria de Colombia. 13. Desarrollo de un Sistema SCADA inalámbrico con Zigbee y Arduino Herrera Jean, Barrios Mauricio y Pérez Saúl. Programa de Ingeniería Mecatrónica, Universidad Autónoma del Caribe 14. Control remoto de un robot móvil LEGO Mindstorm mediante Visión por Computador German Andrés Rivas Lema, Andrés Felipe Gálvez Leyes, Jimmy Alexander Cortes Osorio Universidad Tecnológica de Pereira. 15. Investigación De Nuevos Modelos Nanotecnológicos En El Diseño De Piel Artificial Con Nanoinstrumentación Fabricada Por Electrospinning Para El Recubrimiento De Prótesis De Mano Y Pierna En Discapacitados Antonio Faustino Muñoz, Aldo Pardo García. Universidad Autónoma de Bucaramanga – Grupo de Investigación de Control & Mecatrónica, Universidad de Pamplona Instituto de Investigaciones Tecnologías Avanzadas IIDTA, Universidad del Cauca, Grupo en Automática Industrial
  10. 10. 16. Dispositivo Traductor Del Lenguaje De Señas De Personas Sordas A Sonidos Auditivos De Las Letras Del Abecedario. Carrasco Harold, Encalada Lennin, Universidad Técnica del Norte, Ibarra Ecuador 17. Sistema de seguridad para la conducción de vehículos mediante el análisis facial de una persona utilizando visión artificial Alvaro Fuentes, Gabriela Estrella, Carlos Acosta, Juan Nazate,. Carrera de Ingeniería Mecatrónica, Universidad Técnica del Norte, Ibarra, Ecuador 18. Reconocedor Facial Usando PCA y Redes Neuronales Manuel Alejandro Díaz granados Santos, Universidad Autónoma de Occidente, Cali, Colombia Jesús A. López, Universidad Autónoma de Occidente, Cali, Colombia 19. Diseño e Implementación de un Sistema Bio-inspirado Para la Simulación del Depredador y la Presa Implementado sobre Plataformas Lego Kristel Solange Novoa Roldán, Héctor Iván Tangarife Escobar, Rhonier Ernesto Machado Mosquera Universidad Distrital Francisco José de Caldas, Facultad Tecnológica. Grupo de Investigación Robótica Móvil Autónoma- ROMA 20. Plataforma De Entrenamiento En Tareas De Telecirugía Samuel Quintero M , Oscar Avilés S, Darío Amaya, Robinson Jiménez. Grupo de investigación GAV - Universidad Militar Nueva Granada 21. Diseño y Construcción de una Máquina Llenadora Semiautomática para Bolsa Plástica González Barreto Sergio Fabián, Rodríguez Ardila Julián Felipe Tecnología Mecatrónica, Escuela Tecnológica Instituto Técnico Central 22. Diseño de dispositivo para el control de Multímetros Fluke Serie 80 William Darío Aguirre Hernández, Diego Alejandro Fajardo Vargas, Fabio Lorenzo Roa Cárdenas Ingeniería Mecatrónica, Fundación Universitaria Agraria de Colombia 23. Segway Plataforma De Un Grado De Libertad Kristel Solange Novoa Roldán, Mauricio Diusaba Rodriguez, Yesid Urueña Cuervo Universidad Distrital Francisco José de Caldas, Facultad Tecnológica. Grupo de Investigación Robótica Móvil Autónoma- ROMA
  11. 11. Diagnosis from Chronicles: an overview of related challenges Audine Subias CNRS; LAAS; 7 avenue du colonel Roche F-31400 Toulouse, France Univ of Toulouse, INSA, LAAS, F-31400 Toulouse, France subias@laas.fr - Abstract—Chronicle recognition is an efficient method to address the problem of diagnosis and more generally the problem of situation recognition. Several researches have investigated this direction to develop approaches for dynamic complex systems. But chronicle recognition gathers other interesting research topics related notably to the field of machine learning and to timed transition systems modeling. This article gives a picture of different theoretical and applicative works connected to chronicle recognition which is an active research area. Index Terms—Diagnosis - Chonicle recognition- Diagnosability analysis - Chronicle learning - event(E,t): an event type E is stamped with t the date of its occurrence. noevent(E, [α, β]): this predicate defines a forbidden event. No event E occurs between α and β time units. occurs((m, n), E, [α, β]) : at least m and at most n occurrences of an event E between α and β time units. A notion of domain attribute is also defined by a couple E : e where E is the attribute name and e is a possible value of the attribute. The set of possible values defines the domain of the attribute. A domain attribute as a unique value at each time instant t. In this way, the predicate event(E : (e1, e2), t) models a change in the value of domain attribute E from e1 to e2 at time t and the predicate noevent(E : (e1, e2), [α, β]) forbids the change of value of E between α and β time units. Finally, a set of actions can be launched and some events can be emitted when a chronicle is recognized. Fig 2 gives two simple examples of chronicles according Dousson’s language description. I. C HRONICLES WORLD A. What are chronicles ? Most of the works on chronicles are issued from the French community. [29] has initially developed this model to capture automatically the evolutions or partial evolutions of dynamic systems. The evolutions to monitor are described in terms of temporal patterns called chronicles. A chronicle is not a simple execution trace of the system it is a discriminant observable part allowing to recognize a particular situation. Chronicles are expressed in a specific language and then translated into time constraints satisfaction graphs. The nodes of the graphs are associated to the events, and the edges are labeled by the time constraints (see Fig 1). Chronicle SequenceAB { event(A, t1) event(B, t2) 0 < t2 - t1 < 2 -- sequence within 2s Chronicle Noevent_In_AB { event(A, t1) event(B, t2) noevent(C, (t1 t2) 0 < t2 - t1 < 2 -- sequence within 2s when recognized emit event(C, t2) } [4, 6] [1, 3] B A [2, 5] D when recognized emit event(D, t2) } Fig. 2: Chronicle of sequence AB in [0, 2] (left) and no event C in AB (right). ]0, +∞[ C Chronicle based approaches can be related to other methods to represent situations stressing on the temporal dimension such that situation calculus introduced by [55], the event calculus [51] or the temporal interval of Allen [5],[6]. All these methods are commonly used in the Artificial Intelligence field for representing and reasoning about temporal information. One major advantage of chronicles compared to these approaches is the rich formalism allowing one to describe the observable patterns corresponding to behaviors one wants to detect. In particular, chronicles account for partial orders between events easily and are also able to the lack of events via forbidden events. Another advantage lies on the efficiency of the recognition Fig. 1: A chronicle This kind of approach assumes that a time stamp or occurrence date can be assigned to each event. A chronicle is therefore a temporal pattern described in terms of events and time constraint between event occurrence dates. In [29] the chronicle language is based on the notion of predicate. A predicate defines the events required for the recognition and the events which must be discarded. A chronicle is recognized if all the predicates are satisfied. The major predicates that have been defined are: 1
  12. 12. system which makes chronicles suitable for real-time operation (see section I-B). The main drawback of the chronicle based approach is the design of the chronicles. How acquiring and updating the chronicles? We will see in section III that several approaches have been proposed to remedy this design problem. Chronicle recognition consists in identifying in an observable flow of events all the instances of the chronicles i.e. all possible matchings between an input flow of events and a chronicle. The identification is performed on the fly, as soon as the events occur. When a new event occurs it is integrated into the chronicle if it is consistent with the expected event of the pattern and if its time stamp is consistent with the time constraints of the chronicle. Each new instance of chronicle generated is a new hypothesis and added to the set of hypotheses. The chronicle recognition system must then manage on-line all these instances i.e. all the hypotheses elaborated in time. Instances are discarded when time constraints are violated. Finally, a chronicle is recognized when a complete match is observed. For one given flow of events multiple instances of a chronicle can be recognized in a sequential way or simultaneously. For example, let us consider the simple chronicle defined using the description language previously presented: event(A, t1 ) ∧ event(B, t2 ) ∧ event(C, t3 ) ∧ (t2 − t1 ) ∈ [1, 3] ∧ (t3 − t2 ) ∈ [0, +∞[: an event A occurs followed by an event B with an interval of 1 to 3 time units. B is then followed by an event C (see Figure 3). Let us consider the observed event flow: (A, 0), (B, 2), (A, 8), (B, 9), (C, 11). At t = 11, two instances of this chronicle are simultaneously recognized when the event C occurs (see Figure 4). Note that, in this example the two partial instances (A, 0), (B, 2) and (A, 8), (B, 9) will never be discarded as the event C can occur whenever and the time constraint will never be violated. With an other observed event flow given by (A, 0), (B, 2), (C, 8), (A, 9), (B, 10), (C, 30) two instances of the chronicle would be recognized in a sequential way. A [1, 3] B C Fig. 3: A simple chronicle [29] has developed a Chronicle Recognition System (CRS) that performs an exhaustive recognition. Another chronicle based approach has been developed by [16] providing also a chronicle recognition system called CRS/ON ERA designed on the basis of duplicating automata and able to detect on line chronicle instances (see section IV-D). The main difference between the two approaches is the syntax used to describe the chronicles. Another difference concerns the way the events are managed by the chronicle recognition system. In CRS/ON ERA the events are managed according a first in first out mechanism whereas in CRS the events are managed according to their occurrence dates. The two systems are implemented with object languages and can be integrated as libraries with other applications or used via an  Instances   0   {(A,0)}   2   {(A,0)},{(A,0),(B,2)}   8   {(A,0),(B,2)},  {(A,8)}   9   {(A,0),(B,2)},  {(A,8),(B,9)}   11   B. Chronicle recognition  Time   {(A,0),(B,2)},  {(A,0),(B,2),(C,11)},     {(A,8),(B,9)},  {(A,8),(B,9),(C,11)}   13   {(A,0),(B,2)},  {(A,8),(B,9)}   Fig. 4: Instances of chronicles independent executable. More recently a new recognition tool called Chronicle Recognition Library (CRL) based on the semantic of the chronicle language CRS/ON ERA has been proposed [17]. Chronicle recognition can be related to other situation recognition systems like the temporal diagnosis system developed by [36] for tracking hepatitis symptoms, the work of [57] for planning and matching an observation sequence applied to the diagnosis of CN C-machining centers and also the work of [43] to diagnose trends in growth patterns of pediatric patients. The approach proposed in [49] illustrated in the domain of driverless transport systems must also be mentioned. The difference between these systems concerns mainly the temporal framework used and their ability to represent complex temporal behaviors and complex structured situations. Another relevant criterion is obviously the computational efficiency of the situation recognition system. Plan recognition is another research area that can be related to chronicle recognition. In the context of multi agent systems plan recognition consists in deriving the underlying plan executed by an agent based on partial observation of its behavior. The main difference is that plan recognition generally focusses on the composition of the plan in terms of actions rather than on time aspects. Moreover, the context (goals, preferences and capabilities of the agent, effect of the plan execution...) in which the plan is generated is also considered [60]. II. D IAGNOSIS BASED ON CHRONICLE RECOGNITION When one use chronicle for diagnosis purposes there are two main ways to consider problem. Chronicles can model the normal behavior of the system one wants to diagnose. The diagnosis problem is then tackled as a consistency problem between the observations and the model of the system. In this case, the chronicle recognition allows to detect any discrepancy between the normal behavior of the system and the real behavior given through the observations (that are supposed safe). Another possibility is to consider chronicles of faulty behaviors. The efficiency of such an approach relies on the direct link between the symptom of a fault and the fault itself. Nevertheless, it differs from classical abductive diagnosis systems as time aspects are dominant. Generally diagnosis applications based on chronicles need not only the two types of chronicles (normal and faulty) but also a real chronicle base, what is not trivial to design (see section III).
  13. 13. AUTONOMOUS COMMUNICATING SYSTEMS A. Use of chronicles for diagnosis The chronicle approach has been developed and used in a wide spectrum of applications [22]: for telecommunication systems to manage alarms [27] or in production domain to monitor gas turbines [2]. In the medical field also, for cardiac arrhythmia detection [20], where electrocardiogram interpretation is made by chronicles: a symbolic description with time constraints is associated to pathological situations. In [52] chronicles are used to alarm processing in power distribution systems. More recently chronicles have been used in intrusion detection system. In [56] a chronicle approach for alarm correlation is proposed. Chronicles are not directly used to describe attacks but to represent known phenomena which involve several alarms. Chronicles are used both to represent normal phenomena allowing to discriminate legitimate actions from attacks and malicious (and deterministic) phenomena involving many events are involved. In video understanding and more precisely in the context of visual monitoring applications for human security purposes, a formalism very closed to chronicles is proposed to describe the concepts involved in activity recognition. The objective is to detect suspect human behavior operators [62]. In the field of unmanned aircraft systems U AS, chronicles have been introduced for handling breakdowns and to check the consistency between the activities in U AS [19] but also for the successful deployment of a fully autonomous unnamed aerial vehicle operating over road and traffic networks by detecting vehicles overtaking and passing other vehicles [44]. In the context of high level architecture simulations [11], chronicle recognition is integrated into the development of a simulation as a component to analyze on line the data. Another important field of application of chronicle recognition is collaborative systems notably web services [23]. In this case the main challenge is the distribution of the chronicles into subchronicles and the communication or synchronization mechanisms between the chronicles [13],[40]. The next section gives an overview of two recent projects benefiting from chronicle recognition. B. Chronicles and collaborative communicating systems More and more systems take benefit of communication supports and achieve their objectives in a networked distributed and cooperative way. For these systems, coping with context changes requires considering self-adaptive communication protocols in the design step so that the communication system configuration then dynamically changes according to the user’s requirements and to the load of the communication resources. Dealing with this problem requires the capacity of detecting the possible degradations of the Quality of Service (QoS) and of dynamically modifying the behavior of the communication protocols for each new context situation. This requires in turn both monitoring the QoS values, detecting the degradations, identifying their origins through appropriate diagnosis and executing reconfiguration actions. The DAISY project (Diagnosis for AdaptIve Strategies in collaborative sYstems) tackles the problem of providing adaptability to the traffic control and management system [65]. DAISY focusses on adapting the which mo and relat observing, on the oth Coping with context changes in networked systems requires to provide adaptability to the traffic control and management system. This can be achieved through selfcommunication protocols at the transport level for coping readaptive communication protocols that dynamically with 3. SITUA the dynamically changing contextsystem according from the configure the communication situations arising to the distributed and collaborative mobile applications. user’s requirements and to the load of the communication resources. A taxonomy of transport services provided by the existing 3.1 Princ transport protocols has beenSystem Interconnection showing In the well-known Open elaborated [26] [30], (OSI) that existing transport protocols present several limitations with In the pr referential model (Zimmerman (1980)), composed of seven layers, the application requirements. It layer operating on regard to QoStransport layer is the lowest is important to notice alerting th an end-to-end basis between two based on implementations that most of the transport services areor more communicating a new situ hosts. This layer is located between the applications error where mechanisms offering different functionalities (i.e.and the new si the network layer. Transport services enable applications the QoS p control or congestion control) are merged within the same Therefore, to abstract the communication services and protocols promonolithic implementation. Such a solution has a limited scope to detect vided by the lower network and MAC (Media Access of Control) layers. Transport protocols specifyhandling mech- discrimina applicability and assumes a predefined QoS the mechaanisms already implementedintegrated during the design-time situations nisms to be known and in order to offer the required of transport services. Because theTherefore [67] [66] propose The entire the communication protocol. communication Quality of Service (QoS) is highly impacted by the with a composition analysis o to perform the orchestration of the traffic specific transport protocol in use, our self-adaptation architecture targets of components that provide different and well-identified QoS communic the transport level and proposes to adapt the transport properties to the traffic [30]. This context evolves. approach time serie protocol as the communication component-based be pointed resulting of the combination of pluggable components offering Dealing with this problem not facilitate the a proper specific functionalities, can widely only requires design and characterization of the alternative protocol properties but development of new composed transport services. assess the also the capability of monitoring the QoS to The DAISY context. These are at the basis of the communicationproject suggests the use of chronicles associated to the different relevant modifying the behavior of theto decision to dynamically traffic situations to be detected communication protocol for each new context situation guide the composition of these pluggable components. In other and the objective appropriate reconfiguration actions. words, executing theof DAISY is to guide the reconfiguration strategies. 1 illustrates the architecture [3] foreseen to provide Figure The proposed architecture [4], that has been proposed in (Aguilar-Martin given Figure . a solution to this problem is et al. (2011)), 5and that is foreseen to provide a solution to this problem. • an off analy ficati syste teriza terns corre syste lows of hig event the t sema tions • an on tified deter acqui the s Reco ure 1 This pape following (1) Gene comm (2) Speci The Reconfiguration/Decision System outputs the protothis i col to be deployed. This decision is taken upon several The Reconfiguration/Decision System outputs the protocol is ass inputs: to be deployed. This decision is taken upon several inputs: A cla • the properties the of the different available protocols Po different available protocols gathered comm • the properties of gathered through an ontology fore t through an ontology • the communication context at time t0 Cx (t0 ) chron • • the properties P context at time 0 the communication required by the application and the a seque • the properties context Ca by the application application Pa required Each • • the current context Cx (t) recognized by the Context the current context recognized by the Context Recognition of sa ˆ tive p Recognition System, i.e. Cx (t). System Fig. 1. Architecture for self-adaptation Fig. 5: Architecture for self-adaptation The Context Recognition System monitors and assesses the communication context and related QoS, receiving information from monitors observing, on one hand, the application context Ca and, on the other hand, the network context Cx . The mission of the Context Recognition System is to perform situation recognition; this means to supply to Reconfiguration/Decision
  14. 14. System relevant information every time a new situation arises on the network and also to identify this new situation. A situation is related to an evolution of the QoS parameters of the studied communicating system. Therefore, situation recognition induces the capability to detect different relevant traffic situations taking the discriminating features in terms of QoS indicators of such situations into account. Situation recognition strategy relies then on the analysis of available information issued from the communicating system and is based on two different but non-independent steps: • an off-line step in which historical data are analyzed and processed to characterize the known behavior of the system in terms of chronicles. This step constitutes a learning stage. Learning methods such the one presented in III-B are investigated during this step. The events involved in the chronicles arise from feedback provided by standard parameters stamping the packets. • an on-line step, during which the system characterization and the on line data are used to determine the current expected state of the process. This stage is equivalent to a chronicle recognition step. C. Chronicles and service oriented applications Service Oriented Architecture (SOA) is a software development model in which an application is broken down into small units, logical or functional, called services. SOA allows the deployment of distributed applications very flexible, with loose coupling among software components such as web services, which operate in heterogeneous distributed environments. The services are inherently dynamic and then cannot be assumed to be always stable as the resulting service can be altered by external events such as changes in interfaces, misbehavior during operation etc. In the case of service composition the failure of a single service leads to error propagation in the other services involved, and then to the failure of the whole system. Such failure often cannot be detected and corrected locally (into a single service). It is then necessary to develop suitable architectures for the diagnosis and the correction of failures, both at individual (service) and global (composition) level. To face this problem, an adaptive architecture managing SOA has been proposed in [69]: ARM ISCOM (Autonomic Reflective MIddleware for management Service COMposition) is a Reflective Middleware Architecture. The reflection is the ability of ARM ISCOM to monitor and change its own behavior, as well as aspects of its implementation (syntax, semantic, etc.), allowing the ability to be sensitive to its environment. Based on autonomic computing ARM ISCOM provides an autonomic manager and an ontology framework to manage all the knowledge used by the middleware as depicted in Figure 6. The autonomic manager implements the intelligent control loops according a M AP E (Monitor, Analyse, Plan, Execute, Knowledge) schema [45]. The diagnoser module of this architecture relies on a chronicle recognition system to diagnose failures of the services. The system behaviors associated to these failures are then represented by chronicles designed from the events involved in the choreography of   Fig. 6: MAPE loop in the middleware architecture ARMISCOM the services. The monitor component sends to the diagnoser information related to the event occurrences, which allows the diagnoser to recognize the chronicle instances and to detect and identify failures. To provide a fully diagnosis architecture a diagnoser is associated to each software component of the SOA application. The distribution of chronicles into subchronicles is then one of the problem addressed in this work. Into each diagnoser, a subchronicle is a local chronicle and is defined as a subset of events and/or with a less-constrained time constraints graphs. Elements for synchronization have been introduced into (sub)chronicle definition so that the chronicle recognition process is totally distributed [70]. Each local chronicle involves specific events called linked events that establish the connexions between the subchronicles. Additionally, each event has attributes from which the consistency of the whole operating is checked. ARM ISCOM is deployed in the context of web services in a classical shop application. Two main types of failure are investigated: the violation of an acceptance of service from the warehouse and a delay in the deliverance of a service. The first type of failure is local to a service whereas the second one traduces a failure in the choreography. III. C HRONICLES DESIGN A. Chronicles design methods Diagnosis from chronicles must deal with the problem of the chronicle design as model based diagnosis approaches have to face the model building. Model based chronicle generation approaches have been developed. For instance, in [39] the runs of the monitored system are described in the temporal tiles formalism. The authors propose an algorithm inspired of Petri net unfolding to build all the temporal runs of the system. Then, the projection of these runs on the observable part allows to define the chronicles. Other approaches have been investigated from learning theory. One can consider for instance learning techniques based on Inductive Logic Programming (ILP) [54] [20], case-based chronicle learning of [33],[34] that is a characteristic supervised method by reinforcement learning but also [41],[71],[42] that adapt a
  15. 15. clustering method to learn chronicles in an unsupervised way by projecting chronicle instances into a normative space. Finally, chronicles are also acquired from approaches that analyze logs and extract the significant patterns by temporal data mining techniques. In [32],[31], the chronicle learning problem is motivated by discovering the most frequent alarm patterns in telecommunication alarm logs and their correlations. The tool, called FACE (Frequency Analyzer for Chronicle Extraction), extracts the frequent patterns by carrying out a frequencybased analysis on sublogs, defined on windows of time of fixed duration. The frequency criterion is defined as a userdefined minimal frequency threshold. It is important to notice that the considered patterns refer to a single event. For one event, the alarm log is represented as an histogram of the number of occurrences of the considered event in each time window. Then, a normalization transforms this histogram into a cumulative graph that depicts the sum of the occurrences from the beginning, normalized by the sum of occurrences during the sub-log corresponding to the current time window. Finally a Self Organizing Map (SOM) algorithm [50] is applied to the set of graphs to aggregate different temporal patterns and detect correlations. B. Focus on a chronicle learning approach In the context of the DAISY project that we have presented in Section II-B, the design of the chronicles that model the communicating system situations is a real challenge. An approach has been proposed to learn these situations at the transport level relying on data mining techniques [35][4]. The objective of temporal data mining techniques is simply to discover all patterns of interest in the input data, which is an unsupervised and explanatory task. There are several ways to define the interestingness of a pattern. However a frequency criterium is widely used [28][68][24] for unearthing temporal patterns. Among existing approaches, one can distinguish two main temporal pattern discovery frameworks: frequent episodes [53] and sequential patterns [1]. Frequent episode framework uses a single and long sequence and copes with the discovery of temporal patterns called episodes that occur sufficiently in the sequence. An episode is a partially ordered set of events. The notions of frequent episode and subepisode are defined. In [53] episode discovery focusses mainly on two types of episodes: serial episode when the order between the events is total and parallel episode when there is no order along the events. Sequential patterns framework is based on the discovery in a collection of sequences of all possible time ordered itemsets (i.e. sequence of items) with sufficient support w.r.t the user-defined threshold. The support of an itemset is defined as the number of time the item can be observed in the collection. Further, a sequence is said to be maximal in a set of sequences, if it is not contained in any other sequence. Sequential pattern discovery relies then on the systematic research of maximal sequences that have a support at least equal to the minimal support userdefined. Many methods of search for sequential patterns are designed along the same lines as the Apriori algorithm [1]. For the problem of traffic assessment on great dynamic systems addressed in DAISY , a given situation can be induced by several scenari. For instance, for a fixed network topology and fixed transport level protocols several situations of congestion can occur. If from a static point of view the features of these congestions are similar from a dynamic point of view they are different. It is then necessary to take several scenari of a same situation into account for the learning of the temporal patterns. Therefore, the chronicle discovery approach proposed allows to discover frequent chronicles from multiple sequences that is to say the chronicles that are frequent in each sequence and not on only on the collection of sequence as in sequential pattern approaches. Moreover, the proposal is to discover the chronicles not only for a given frequency criterium but for all the possible frequencies. The goal of the proposed algorithm [35] is to build large frequent chronicles in an event log composed by multiple runs. The chronicles are built by incrementation. At each step, candidate chronicles of size i are built from frequent chronicles of size i − 1 and are kept if they are frequent as well [24]. A frequent chronicle is a chronicle for which the frequency of appearance in the sequence is larger than a user defined threshold. When a larger frequent chronicle cannot be found, the search is stopped. In the final set of kept chronicles, there is no chronicle that can be a subchronicle of another one. This means that the algorithm returns the longest frequent chronicles in the event flow. IV. C HRONICLES AND OTHER FORMALISMS Chronicles are temporal pattern and then can be viewed as timed transition systems. From a diagnosis point of view, the interest is as we will see in section V to apply well known approaches of diagnosability analysis developed on such systems. By using timed transition systems such as Time Petri Nets (T P N ) [59], one can take benefit of the different methods of model checking to explore the state space of the system and to check established properties. Thus, several works have been developed to translate or to transform chronicles. A. Chronicle modeling based on Time Petri Nets Time Petri Nets (TPNs) is a prominent tool to model timed discrete event systems as several effective analysis methods have been proposed [59][9]. T P N s extend Petri nets with temporal intervals associated to transitions. Firing delay ranges are associated to transitions. The work developed by [63] focusses on the transformation of chronicle to T P N models. They consider specific chronicles called Causal Temporal Signature (CT S) that are expressed by a conjunction of triples (A, B, T ) where A and B are events and T a time constraint. The authors consider three types of time constraints which are defined by interval structure: date, time and duration. A date structure is used to model the time between the occurrence of two events whereas a delay structure allows to model the time between the occurrences of two events taking into account a degree of uncertainty. Finally, the duration structure is similar to a ”hold” predicate as it is used to express that information is true from a date and remains true throughout the time interval involved. The problem addressed in this work concerns the
  16. 16. consistency checking of a chronicle base. Generic models based into a measure of the possibility. Global constraints are then T P N are proposed to model CT S. The modeling is based on checked through a membership test of a measured duration to a strong semantic of Time Petri Nets that means that an enabled a set of value ranges. The modeling of the chronicles relies on transition check the two boundsenabling time. To illustrate thisoccurs before time date ‘δ’. [15]. The joint there of both types of fuzzy p-t the Petri nets Otherwise, if use is no clocks to is fired at its latest modeled respectively by Tmin approach,. one of Į t.u, Tmin is model due chroniclestrong occurrencenets D, according with the global model comparing to the Petri of allows to simplify STS semantic, there is a and Tmax After the proposed fired of to the is given Figure 7. Additionally, some rules are proposed to construct afiringuse Tmax which is also a success in this p-time). In a first step a semantics. of of one type of Petri net (time or case. Petri net model is associated to each constraint of a chronicle. Then a merging procedure is defined to obtain the resulting C. Rules to construct a global model of a set of chronicles Petri net in which there is no structural conflict situation as Let’s considers the chronicle form given by equation (4). NOP in this approach each constraint is verified independently. The This general chronicle illustrates that the same event can [0, +∞] possibility functions are integrated to the subchronicles models A occurs many times. The objective now is to specify rules to associating them to the firing of each transitions. A possibility make clear how these events should be interpreted in order to Max Min_b function is an associative function that establishes a possibility translate the chronicle to TPN. (A,B, [α, β])* (A,C, [γ, Tmin value (fuzzy number) to the marking of the place. An example [Į, Į] AB δ])*(C, not D, [δ, λ]) -> G (4)is given Figure 8. It corresponds to a of chronicle modeling Rule 1: Fusion of two occurrencesevents noted event ej linked Min_a global constraint between two of a referent ei and When a referent event appears in many triplets (case of [0, +∞] by an interval constraint defined by the bounds dj,i and fj,i . ] β- Į, β - Į +1] Tmax B event ‘A’ in the first two triplets of equation (4)), it must be The communication delay between the two sites is bounded in modeled by a single transition in the TPN model. [δm , δM ]. The site receiving an image of ei (noted ek) is in Success (A,B, [Į, β]) 14 Rule 2: Fusion of occurrence of referent event and charge of the constraint verification. The function µA represents Fig. 5. TPN model of a delay secondary event Fig. 7: Directly from [63]: a chronicle of a delay between the membership function of the duration between the occurrence When an event how the as a referent event and as a In the following we showappears fuzzy p-t time Petri nets are used for modelling the different events A and B of ej ‘Min_a’ is then marked and enables the transition 'B'. If B secondary and ek to the fuzzy set of real values which allow to verify event, types of globalconstraintcorresponding triplets must be put in variable the constraints whichwhich is defined by the linguistic and can compose a sub-chronicle. occurs before the firing of Tmax, then the corresponding sequence by the fusion of events in a single transition (case ”verification of the constraint”. transition is from In this chronicles. The resulting marked global model fired. a set ofcase, the place 'Success' isglobal Timeof the event C in the last two triplets). The firing of a transition is C. Globalvalidated by the modelling by of local events and of image of interval constraint reception considering a delay indicating then analyzed with model checker to (2) is Petri Net isthat the relationship agiven by equation check the events external of a site. This representation is inspired from checked. Otherwise the firing of Tmax indicates the failure of The consistency of the chronicles by detecting input flows of events figure 11 gives the corresponding fuzzy p-t time Petri net. NOP Petri nets with internal synchronization representation [61]. the can abnormally lead to the recognition its properties that recognition. In order to be able to check of the chronicle. A Min_b1 Max1 [Į, Į] AB S1 Min_a1 B ] β- Į, β- Į +1] S1 Min_b2 Max2 (A,B, [Į, β]) S2 ] 0, 1] (A,C, [γ, γ]) [γ, γ] Ac Min_a2 G such as liveness illustrated in this PN of a loopback arc The approach is and boundness,the field hasproduction system from Tmax. This allows us constructing the state class diagnose on a conveyor belt. Chronicles are introduced to graph and then to like that this PN switch. The analysis componentsprove sensors and is live and bounded. performed on the models aims to check the consistency of the faulty NOP behaviors that will be recognized through the chronicles. [dj,i C Min_b3 Max3 CD [δ, δ] B. Chronicle modeling +∞] Fuzzy Petri Nets T [0, based on C Min_a3 ] λ- δ, λ- δ +1] In [12] [14] attention is focussed on distributed discrete D D CD Ma Min_b events systems. Therefore, a chronicle model is decomposed x (C,not D, [δ, λ]) into a set of subchronicles distributed on several sites. Moreover, S3 [δ, δ] Fig. 8: Directly from [12]: a chronicle Caseaofdelay between two of failure the authors consider the problem of communication delays Consequencefrom two distinct sites events G of between sites as in real applications messages transmission Modelling principles Fig. 11. constraint type Min_a between sites are not instantaneous. Thus, the time constraints of T2 Fig. the chronicles never refers to a[0, +∞]time reference (local clock) 7. Construction rules Application local T1 The function represents the membership function of the duration λ- δ, λ- δ +1] [0, +∞] C. Chronicle modeling based on Labeled Time Petri Nets with or to an] implementation architecture. A constraint traduces a We also specify rules fordefined by the linguisticfailure or the management of variable ”verification of the to the Priorities fuzzy set causal relationshipSuccess considered application e.g. a transport of the ;C, not D͕ ΀δ͕ λ΁Ϳ success during the recognition of a chronicle. constraint”. In [37] a systematic method to switch from any chronicle duration between two sites. The authors consider precedence Fig. 6. TPN Model of duration Rule 3: Modeling of a recognition failure constraints that specify that an eventfrom the period.after the marking of corresponding Labeled Time Petri in the with Priorities figure 11, Net interval The date case is deduced directly A must occur The The In to the a place isthe token of the state of the recognition [0, 1]. On case of failure, associated to a fuzzy value event Δt is interval to an equal lower and that an eventdelay place (LTassociated to a proposed. the firing of (occurrence modeling the probability P removed using a Labels are used for of date B, reduced constraint meaning upper bounds A mustprocess is N P r) is token from sinck transition (transition) and whose the is occur after the event B in the interval [α, β] and window events in the arc). (p= [Δt, Δt]). without any output chronicle and priorities are introduced to manage can be equal to 1. The fuzzy value , associated to the marking of the place B, will be admissibility when an event A must occur after the occurrence conflicts due to time evolution. Indeed, (C, not D, [δ, λ]) (3) Rule 4: Modeling of a recognition success Time Petri Nets with by using : of the last event precedingcase and in duration constraint. Itcalculated Prioritiesthe following rule extension of T P N in which a priority it, of the the interval [α, β]. The (TPNPr) is Equation (3) gives a use It associates a place anto each successful triplet’s distribution the event D does not occur between the date δ delays of a chronicle and the consideration of t.u relation on transitions defined [7]. part of a CTS means that recognition. When all tripletsisin the premise Considering weak time (3) induces to distinguish two types of constraints: semantic the time elapse can are increase the number and λ t.u after the occurrence of C. The semantic of this are recognized, their consequencesonly deduced by enabling of firable The local constraints link events dated by a same site. represents weighting of transitions i.e.a the firing proportional to the importance of the event a triplet is given by figure 6. In this model, only transitions T1where single transition (see figure 7). a transition cannot be forbidden by occurrence - T2 are commented. The other nodes are similar by thosein the sub-chronicle elapse. Priorities are used toweighting isfiring by an expert who judges The global constraints link events dated to different the time instance to be recognized. This complete fixed conditions. and monitoring sites. In a T are more transition t may fire from a state = (m, I) V. APPLICATION TO CONVEYOR STUDY CASE seen in Figure 5. T1 models the occurrence of the event D inthat certain eventsP N P r a meaningful than others. For example, wescould consider that the The interval of the constraint. introduce anisuncertainty on the communication delays Therefore, it a failure as the (where m is a marking and I a function associating a time the To illustrate event ”wagon on a our method, let us consider the given example section”. constraints verification of the sub-chroniclerecognitiontranslatedof figure 8. plot” isamore meaningful than the eventThis is enabled by theThe formula interval It is part of a belt conveyor. ”wagon in new to each transition enabled by m) if t type of event D is denied in the triple and then the which is fails. is superior if and minor in 3, shows that Otherwise, T2 firing models a success it a situation where D conveyor includes a to which is continuously the other case.aIt allows to increase the belt actuated by P H I i b f ` Whgb a ` YX V X c V b b f ` Wedb a ` YX WU CSQ V X c V V T R r qp a r` r Ss rt p a us rSs bƒ a bR x wr V T a ¥Cw‚¥x V€ SS yQ a)Ss vdus a` †‡©g¥x … „ a r s 'R Q r s ˆ as probability associated to the sub-chronicle instance to be recognized while taking into account 1523 26, 2003 September DRAFT
  17. 17. marking m, firable instantly and if no transition with higher priority satisfies these conditions. The authors formally define in the T P N P r formalism several basic patterns from which a chronicle model can be composed. These patterns correspond to common structures in a chronicle such that: - event(a, t) ∧ t ∈ [α, β[:] an event a occurs between α and β time units; event(a, t) ∧ noevent(b, [0, t[):] an event a occurs without any prior event b; event(a, t) ∧ occurs((m, n), b, [0, t[):] an event a occurs after at least m and at most n events b; Fig. 9 gives the LT P N P r of a chronicle based on the two predicates: event and occurs. The chronicle is recognized if the “pok ” places is marked. Initially, the place pinit is marked and m events of type b are expected to fire the transition t2 . Then, if at least n − m + 1 events b occur — i.e. a total of at least n + 1 events b — the chronicle is not recognized. On the other side, if an event a occurs before the n − m + 1 events b, then the chronicle is recognized. Finally, priorities (dashed edges) ensure compliance of the LT P N P r with the chronicle bounds. pinit t1 b p1 [0, 0] m t2 b p3 [0, 0] p4 t4 n−m+1 tok a pok Fig. 9: event(a, t) ∧ occurs((m, n), b, [0, t[) Three types of systematic combination templates of these patterns are also considered: - D. Chronicle Recognition modeling based on Coloured Petri Nets The objective of the work [10][11][18][21] is for a given chronicle to establish in a progressive way the list of all its recognitions. A chronicle is defined as a single event, the conjunction of two chronicles, the disjunction of two chronicles, the sequence of two chronicles or the absence of a chronicle during another chronicle. This is noted by: C ::= event CC|CC C C (C) − [C]. The approach is based on the CRS/ON ERA [16] chronicle description language that introduces several operators such as: - p2 t3 The translation of any chronicle model will consist in recursively applying the correct pattern to the chronicle model. For instance, the translation of C2 consists in applying sequence to C1 first and then divergence. In this way, the LT P N P r of any chronicle results from the representation of a basic pattern or the result of a previous combination. The approach is applied in the context of diagnosability analysis (see V), therefore the Petri Net represents the chronicle model and the part of the recognition language that is relevant to diagnosability. In this work, chronicle model provides the shortest words of the recognition language that are considered as a faulty (or normal) behavior of the monitored system. The modeling of the chronicle recognition is also a challenge shown in the next section. sequence: n fully ordered patterns, for example a sequence of two events C1: event(a, t1 ) ∧ event(b, t2 ) ∧ t2 − t1 ≥ 4. divergence: an initial shared pattern precedes n parallel patterns, for example C2: event(a, t0 ) ∧ event(b, t3 ) ∧ C1 ∧ t1 − t0 ≥ 4 ∧ t3 − t0 ≥ 5. convergence: n parallel patterns precede a final shared pattern, for example C3: event(a, t0 )∧event(b, t3 )∧ C1 ∧ t3 − t0 ≥ 2 ∧ t2 − t3 ≥ 5. disjunction: C1 C2, C1 or exclusive C2 conjunction C1C2, C1 and C2 can occur in any order sequence C1C2, C1 then C2 absence (C1) − [C2], C1 without any occurrence of C2 during the recognition of C1. For instance, the chronicle (EF ) − [G] corresponds to the sequence EF (event E followed by event F ) without event G occurring between E and F . The modeling of the recognition relies on the modeling of the different operators with Coloured Petri Nets (CP N ) that extend classical Petri Nets with colors assigned to the tokens [46]. The places of the net are typed i.e. a color set is assigned to each place and then a place contains multisets of tokens. It is then possible that the enabling of a transition depends on the input marking i.e. on the colors of the token of the input places. Input and output edges of transitions are labelled by expressions in which variables of certain type are involved. The firing of the transition supposes then a binding of the variables to the colors of the tokens such that the transition is enabled. The basic principle of chronicle recognition modeling proposed is that the places are used to store in a single token a list of all chronicles instances. A chronicle instance is represented by a list of events, and a chronicle recognition set by a list of lists of events. The authors mention that these coloured nets are designed in such a way that they can be easily composed with others by using place fusion to consider complex chronicles. Figure 10 presents the CP N for a simple chronicle corresponding to the occurrence of events A and B.
  18. 18. occurrence of event b, but this will “complete nothing” and leave unchanged the marking. Now as soon as there is an event a recognition, it will be completed with each event b recognition to produce the recognitions of chronicle A B. B a ⎥ ⎢ ⎢ ⎥ ⎣ instB ⎦ →⎣ ⎦ instB 2 Num cpt⎤ ⎡cpt [[]] cpt+1 )⎥ End⎢ ANR(currB , Ea ⎥ →⎢ ⎣ instB of two⎦events. By introducing time, between the occurrence cpt + 1 the objective is to determine whether durations between events Success must be taken into account by the diagnostic tool to improve When the event is a B occurrence and 2 SuccessA = [], the overall diagnosability [48] then the content of place Success (on the right hand side net) Chronicles can be used to gathered both the by now evolves to include all new occurrences of AB obtainedknowledge about the underlying system and aboutA occurrences. combining the last B event with all previous the faulty executions. The underlying system is then supposed to behave like a timed m modelabM = ⎤ ⎡ ) with E the set of dated event⎤ (E, T trajectories ⎡ [[]] of Start the system (i.e. the system language) and T the set of time [[]] ⎥ curr dates of the events. 2 SuccessA⎢ currB ⎥ B ⎢ constraints between the occurrence B ⎥ ⎥→⎢ ⎢ ⎣ ANR(instB CPR([[E cpt+1 ]], curr the Supervisory Success ⎣ instB ⎦ this track we, present the work ofB )) ⎦ To illustrate b Num 2 Control cpt cpt ⎡(DISCO) team of LAAS − CN RS. ⎤ [[]] In [58], the set of trajectories of the system leading to the ⎥ curr End⎢ recognition of the chronicleBc is called the recognition language ⎥ →⎢ ⎣ ANR(instB , CPR([[E cpt+1 ]], currB )) ⎦ b L(c). Each chronicle is associated to its observable recognition cpt + of language C, that is the set 1 observable projections (i.e. the Disjunction recognition Fig. CPN for recognition Fig. 10: The CP N 3.model for A Bthe recognition of the AB E. trajectory restricted to the observable events) of any trajectory of construction of the coloured Petri fault f (i.e. a fault TheL(c). Each abnormal situation or net for disjunction event chronicle (directly issued from [18]) Note also that both nets for A and B have variables curr like in [64] or a fault pattern like in [47] or in nets is very straightforward: places Start and Success of both [48]) has a and inst. In order to distinguish them when dealing with are signatureand both nets work in parallel. We do not go into system merged, Sig(f ) that is the observable behavior of the the overall marking, they will be denoted respectively currA , further detail as to this operatorchronicle model c(f ) is associated when the fault occurs. A for brevity reasons. The and currB , instB . instA authors define the operational semantics of chronicle to a fault f when its observable recognition language C is a recognition modeled using CP N for the different constructions 8 We provide here a simplified definition of complete, while its full f subset of the fault signature C account the case 7 When A and B are the same name, we deal with “chronicles with definition includes a mechanism to take into f ⊆ Sig(f ). with repetition, of the language and prove that the recognition provided by the repetitions” which are taken into account homogeneously in our processing. e.g. chronicle AA. Under the single fault assumption, the authors propose to CP N is a suitable representation of the recognition for the check the diagnosability of a fault f relying only on a set of CRS/ON ERA language. The modeling of the recognition is chronicles by checking whether two chronicles c(f ) and c(f ) of main interest for formal verification activities in critical and are exclusive or not. Two chronicles are defined exclusive if 104 large scale systems. The work has been applied in the context they cannot be recognized with the same flow of events. of simulation data analysis notably in an airport simulation to The proposed exclusiveness analysis is performed in the detect faulty simulation activities [21]. following way: V. D IAGNOSABILITY A NALYSIS BASED ON CHRONICLES Check for the non exclusiveness of chronicles c(f1 ) and c(f2 ): if Cf1 ∩ Cf2 = ∅ then f1 and f2 are not Nowadays, the design of a posteriori tool for the diagnosis diagnosable. of a system is no longer imaginable as systems gets more and Check for the non exclusiveness between a chronicle more complex. It is imperative to be concerned at the design c(f ) and a non faulty model of the monitored system stage of the system diagnosis purposes to achieve. Several c(f0 ): if Cf ∩ Cf0 = ∅ then f is not diagnosable and research works tend to characterize and analyze properties the more precisely f is not detectable. system should have to make the diagnostic tool efficient on-line Note that in the case where Cf = Sig(f ) checking for the to monitor and isolate the faults of the system: among these exclusiveness allows to conclude on the diagnosability property. properties, diagnosability is the most studied one. Diagnosability This approach has been applied in the context of the is closely related to the capability of the monitoring to record W S − DIAM ON D european project with the objective to observations which are necessary to determine the failure causes develop self-healing Web Services [25]. More recently, [37] within the system with certainty. In case of dynamic systems, propose a fully automated and formal method to perform these diagnosability analysis usually consists in analyzing, off-line, exclusiveness tests. This method relies on three main steps as the system trajectories with respect to their observability to illustrated on Figure 11: determine whether the corresponding diagnostic tool will be During the translation step each chronicle model is able to diagnose the faults online. In the context of discretemodeled into a Labeled Time Petri Net with Priorities event systems (DES) the work developed by [64] introducing (LTPNPr). according the method we have depicted in the diagnoser notion is a reference. section IV-C. So, an intuitive way to tackle diagnosability is to answer the Then a Product step aims to construct from the question: is there a diagnosis function which from an observable LT P N P r model of each translated chronicle a behaviour of the system can tag the observable behaviour with unique LT P N P r (called product) that models the a label that is either, ”normal”, ”faulty” or ”ambiguous” and possible common behaviors with synchronized events. this in a bounded delay?. For Timed Discrete Event Systems (TDES). The aim is to Indeed, the exclusiveness test aims to check that improve the characterization of diagnosability for a discrete the chronicles cannot be recognized by a common event system by taking into account the notion of finite durations trajectory of events.
  19. 19. General principle of exclusiveness analysis based on chronicles The system (E , T ) Chronicles Database Projection Data Translation (Eobs , Tobs ) LTPNPrs Product LTPNPr Steps Generation SCG Analysis Exclusiveness? Analysis Results LTPNPr: Labeled Time Petri Nets with Priorities SCG: State Class Graph Fig. 11: General principle of exclusiveness analysis based on chronicles - Finally, an Exclusiveness analysis step performs the exclusiveness tests. The exclusiveness analysis must deal with an important number of trajectories that means chronicle instances that may induce the chronicle recognition. These chronicles instances correspond to the marked behaviors of the Petri Net. By introducing time intervals between two events, the state-space associated to the set of possible trajectories is usually infinite. In order to perform any diagnosability analysis, it is then necessary to use an abstraction of this state-space as the complete enumeration of the possible instances of each chronicle is not realistic. The authors propose to consider a time abstraction through the State Class Graph (SCG) of Time Petri Nets [9] and to perform the exclusiveness analysis on this time abstraction. Given SCf = {c1 (f ), . . . , cn (f )} a set of chronicles associated to a fault f and a set of chronicles SCf = {c1 (f ), . . . , cn (f )} associated to a fault f . As previously explained checking the non exclusiveness between at least one element of SCf and one element of SCf allows to conclude to the non diagnosability of the faults f and f . Then, in a first stage from the SCG the set of trajectories leading to the recognition of the two chronicles noted WOK . WOK is the set of trajectories leading to the marking of the pok places of the chronicle models (see section IV-C). Then, each of these trajectories is compared to the set of observable trajectories that the system can generate (without taking the events date into account) noted WOBS . If WOK WOBS = ∅ then the two chronicles are exclusive. If furthermore the faulty behavior associated to f (resp f ) is totally recognized by SCf (resp SCf ) then the system is diagnosable. If WOK WOBS = ∅ the two chronicles are not exclusive and the two faults f and f are not diagnosable. The solution of the inequalities system TOBS ∧ Tc gives the precise intervals where the two chronicles are not exclusive. With Tc the time constraints on the paths leading to the recognition of both chronicles and TOBS the restriction of the time constraints of the chronicle to the observable events. To validate the proposed approach a diagnosability checker tool has been developed, based on TINA (TIme Petri Net Analyzer, (http://www.laas.fr/tina)) [8]. The main difficulty of this approach is the combinatory explosion intrinsic of the analysis. Therefore in [38] the authors propose an approach based on Petri nets unfolding to face this problem by benefiting of a representation as a partial order of events. Current work address the problem of checking diagnosability of patterns in discrete-event systems. A pattern is a partial order of observable/no observable events and is then 8 / 20 very similar to chronicles. VI. C ONCLUSION This paper provides an overview of research investigated in the field of chronicle recognition. As we have tried to point out, the chronicle recognition is used in a wide range of applications from medical diagnosis to reconfiguration of communication protocols. One major advantage of chronicle recognition notably for diagnosis purposes is the efficiently on line. The approaches developed to translate chronicles into others formalism like Petri nets allow to use model checking technics for formal verification purposes. Moreover, the design of the chronicle takes benefits of a large amount of works developed in the data mining community. This picture allows us to identify a number of research issues. We can highlight for instance the chronicle design recovery issued from the diagnosability analysis based on chronicles. The problem of on line learning of chronicles is also an interesting challenge as the problem of the chronicle distribution in the context of distributed applications. VII. ACKNOWELEDGEMENT I want to strongly thank the members of the Diagnosis and Supervisory Control (DISCO) team of the LAAS-CNRS, the colleagues of the DAISY project and of the Post Graduate Program with Universidad de los Andes de Merida - Venezuela involved in many works presented in this paper. R EFERENCES [1] R. Agrawal and R. Srikant, “Mining sequential patterns,” in Proceedings of the Eleventh International Conference on Data Engineering, ser. ICDE ’95, Washington, DC, USA, 1995, pp. 3–14. [2] J. Aguilar, K. Bousson, C. Dousson, M. Ghallab, A. Guasch, R. Milne, C. Nicol, J. Quevedo, and L. Trav´ -Massuy` s, “Tiger: real-time situation e e assessment of dynamic systems,” LAAS-CNRS, Toulouse, France, Technical report, 1994. [3] J. Aguilar, A. Subias, and L. Trav´ -Massuy` s, “Situation assessment e e in autonomous systems,” in Global Information Infrastructure and Networking Symposium ( GIIS ), Choroni (Venezuela), december 2012. [4] J. Aguilar, A. Subias, L. Trave-Massuyes, and K. Zouaoui, “A chronicle learning approach for self-adapting strategies in collaborative communicating systems,” LAAS-CNRS, France, Technical Rapport 1xs1698, October 2011. [5] J. F. Allen, “Maintaining knowledge about temporal intervals,” Communications of the ACM, vol. 26(11), pp. 832 – 843, 1983. [6] ——, “Towards a general theory of action and time,” Artificial Intelligence, vol. 23(2), pp. 123–154, 1984.
  20. 20. [7] B. Berthomieu, F. Peres, and F. Vernadat, “Bridging the gap between timed automata and bounded time Petri nets,” in Proceedings of Formal Modeling and Analysis of Timed Systems (FORMATS’06), LCNS 4202, 2006. [8] B. Berthomieu, P. Ribet, and F. Vernadat, “The tool tina – construction of abstract state spaces for Petri nets and time Petri nets,” International Journal of Production Research, vol. 42, no. 14, 2004. [9] B. Berthomieu and F. Vernadat, “State class constructions for branching analyzis of systems,” in Tools and Algorithms for the Construction and Analysis of Systems (TACAS’03), Warsaw, Poland, 2003. [10] O. Bertrand, P. Carle, and C. Choppy, “Chronicle modelling using automata and coloured Petri nets,” in 18th Internation Workshop on Principles of Diagnosis (DX’07), Nashville, TN, USA, 2007, pp. 243– 248. [11] ——, “Towards a coloured Petri nets semantics of a chronicle language for distributed simulation processing,” in In CHINA 2008 Workshop (Concurrency metHods: Issues aNd Applications), 2008, pp. 105–119. [12] A. Boufaied, A. Subias, and M. Combacau, “Chronicle modeling by Petri nets for distributed detection of process failures,” in IEEE International conference on systems, man and cybernetics (SMC’02), Hammamet, Tunisie, 2002, pp. 243–248. [13] ——, “Distributed fault detection with delays consideration,” in 8th World Multi-Conference on Systemics, Cybernetics and Informatics (SCI’2004), Orlando,USA, july 2004, pp. 135–140. [14] ——, “The distributed time constraints verification modelled with time Petri nets,” in 17th IMACS World Congress on Scientific Computation, Applied Mathematicsand Simulation (IMACS05), Paris, France, july 2005. [15] J. Cardoso, R. Valette, and D. Dubois, “Fuzzy Petri nets: an overview,” in 13th IFAC World congress : IFAC’96, vol. J, San Francisco, USA, july 1996, pp. 443–448. [16] P. Carle, P. Benhamou, M. Ornato, and F. Dolbeau, “Building dynamic organizations using intentions recognition,” Office national d’tudes et de recherches arospatiales, Chˆ tillon, France, Tech. Rep. ONERA-TP a 99-136, 1998. [17] P. Carle, C. Choppy, R. Kervac, and A. Piel, “Handling breakdowns in unmanned aircraft systems,” in 18th International Symposium on Formal Methods - Doctoral Symposium, Paris,France, august 2012, pp. 286–291. [18] P. Carle, C. Choppy, and R. Kervarc, “Behaviour recognition using chronicles,” in Proceedings of the 2011 Fifth International Conference on Theoretical Aspects of Software Engineering, ser. TASE ’11. Washington, DC, USA: IEEE Computer Society, 2011, pp. 100–107. [19] P. Carle, C. Choppy, R. Kervarc, and A. Piel, “Safety of Unmanned Aircraft Systems Facing Multiple Breakdowns,” in 1st French Singaporean Workshop on Formal Methods and Applications (FSFMA 2013), ser. OpenAccess Series in Informatics (OASIcs), C. Choppy and J. Sun, Eds., vol. 31. Dagstuhl, Germany: Schloss Dagstuhl– Leibniz-Zentrum fuer Informatik, 2013, pp. 86–91. [Online]. Available: http://drops.dagstuhl.de/opus/volltexte/2013/4093 [20] G. Carrault, M.-O. Cordier, R., M. Garreau, J. Bellanger, and A. Bardou, “A model-based approach for learning to identify cardiac arrhythmias,” in AIMDM, 1999, pp. 165–174. [21] C. Choppy, O. Bertrand, and P. Carle, “Coloured Petri nets for chronicle recognition,” in Proceedings of the 14th Ada-Europe International Conference on Reliable Software Technologies, ser. Ada-Europe ’09. Berlin, Heidelberg: Springer-Verlag, 2009, pp. 266–281. [22] M.-O. Cordier and C. Dousson, “Alarm driven monitoring based on chronicles,” in 4th Sumposium on Fault Detection Supervision and Safety for Technical Processes (SafeProcess), Budapest, Hungary, june 2000, pp. 286–291. [23] M.-O. Cordier, X. L. Guillou, S. Robin, L. Roz´ , and T. Vidal, “Distributed e chronicles for on-line diagnosis of web services,” in 18th International Workshop on Principles of Diagnosis, G. Biswas, X. Koutsoukos, and S. Abdelwahed, Eds., May 2007, pp. 37–44. [24] D. Cram, B. Fuchs, Y. Pri, and A. Mille, “Dcouverte complte et interactive de motifs temporels avec contraintes numriques partir de squences d’vnements,” in Atelier ”Fouille de donnes temporelles et analyse de flux de donnes”, EGC2009, 2009. [Online]. Available: http://liris.cnrs.fr/publis/?id=3786 [25] W.-D. D4.2, “Specification of diagnosis algorithms for web-services phase 1,” WS-DIAMOND European project, Tech. Rep., September 2006. [26] C. Diop, E. Exposito, C. Chassot, and K. Drira, “Autonomic service bus,” in 6th International Conference of Interoperability for Enterprise Systems and Applications (I-ESA12)Workshop Factories of the Future (FoF), Valencia - Spain, march 2012. [27] C. Dousson, “Alarm driven supervision for telecommunication networks:iion line chronicle recognition,” Annales des T´ l´ communications, vol. 51 ee (9-10), pp. 501–508, 1996. [28] C. Dousson and T. V. Duong, “Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems,” in IJCAI 99: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, San Francisco, CA, USA, June 1999, pp. 620–626. [29] C. Dousson, P. Gaborit, and M. Ghallab, “Situation recognition: representation and algorithms,” in IJCAI: International Joint Conference on Artificial Intelligence, Chamb´ ry, France, august 1993, pp. 166–172. e [30] E. Exposito, C. Chassot, and M. Diaz, “New generation of transport protocols for autonomous systems,” in IEEE Global Telecommunications Conference (Globecom 2010), Miami, Florida (USA), december 2010. [31] F. Fessant and F. Cl´ rot, “An efficient som-based pre-processing to e improve the discovery of frequent patterns in alarm logs,” in DMIN, 2006, pp. 276–282. e [32] F. Fessant, F. Cl´ rot, and C. Dousson, “Mining of an alarm log to improve the discovery of frequent patterns,” in Industrial Conference on Data Mining, 2004, pp. 144–152. [33] M. W. Floyd and B. Esfandiari, “A case-based reasoning framework for developing agents using learning by observation,” in ICTAI, 2011, pp. 531–538. [34] M. Floyd, M.Bicakci, and B. Esfandiari, “Case-based learning by observation in robotics using a dynamic case representation,” in FLAIRS Conference, 2012. [35] B. Frances, “Apprentissage de chroniques pour les rseaux de communications autonomes,” LAAS-CNRS, France, Master, Toulouse 3, Paul Sabatier University, September 2012. [36] J. Gamper and W. Nejdl, “Abstract temporal diagnosis in medical domains,” in Artificial Intelligence in Medicine, vol. 10(3), 1997, pp. 209–234. [37] H. Gougam, A. Subias, and Y. Pencole, “Timed diagnosability analysis based on chronicles,” in 1IFAC International Symposium on Fault Detection Supervision and Safety of Technical Processes (SAFEPROCESS’2012), Mexico (Mexique), august 2012, pp. 1256–1261. [38] ——, “Supervision patterns: formal diagnosability checking by Petri net unfolding,” in 4th IFAC Workshop on Dependable Control of Discrete Systems (DCDS 2013), York (UK), sep 2013. [39] B. Guerraz and C. Dousson, “Chronicles construction starting from the fault model of the system to diagnose,” in International Workshop on Principles of Diagnosis (DX04), Carcassonne, France, 2004, pp. 51–56. [40] X. L. Guillou, M.-O. Cordier, S. Robin, and L. Roz´ , “Chronicles for e on-line diagnosis of distributed systems,” in 18th European Conference on Artificial Intelligence, ECAI08, Patras, Greece, july 2008, pp. 194–198. [41] T. Guyet and R. Quiniou, “Mining temporal patterns with quantitative intervals,” in ICDM Workshops, 2008, pp. 218–227. [42] ——, “Extracting temporal patterns from interval-based sequences,” in IJCAI, 2011, pp. 1306–1311. [43] I. J. Haimowitz and I. S. Kohane, “Automated trend detection with alternate temporal hypotheses,” in In Proceedings of the International Joint Conference on Artificial Intelligence IJCAI, Chamb´ ry, France, e 1993, pp. 146–151. [44] F. Heintz, “Chronicle recognition in the WITAS UAV project a preliminary report,” in Swedish AI Society Workshop (SAIS2001), 2001. [45] IBM Corp., An architectural blueprint for autonomic computing. USA: IBM Corp., Oct. 2004. [Online]. Available: www3.ibm.com/autonomic/pdfs/ACBP2 2004-10-04.pdf [46] K. Jensen, “An introduction to the practical use of coloured petri nets,” in Petri Nets (2), 1996, pp. 237–292. [47] T. J´ ron, H. Marchand, S. Pinchinat, and M.-O. Cordier, “Supervision e patterns in discrete event systems diagnosis,” in WODES06: Workshop on Discrete Event Systems, 2006. [48] A. Khoumsi and Ou´ draogo, “Diagnosis of faults in real-time discrete e event systems,” in SAFEPROCESS’09, Jun 2009, pp. 1557–1562. [49] S. Kocksk¨ mper and B. Neumann, “Extending process monitoring a by event recognition,” in In Proceedings of the Second International Conference on Intelligent Systems Engineering (ISE’94), 1994. [50] T. Kohonen, “Self-organized formation of topologically correct feature maps,” Biological Cybernetics, vol. 43, no. 1, pp. 59–69, 1982. [51] R. Kowalski and M. Sergot, “A logic-based calculus of events,” New Generation Computing, vol. 4(1), pp. 67–95, 1986. [52] P. Laborie and J.-P. Krivine, “Automatic generation of chronicles and its application to alarm processing in power distribution systems,” in 8th international workshop of diagnosis (DX97), Mont Saint-Michel, France, 1997.
  21. 21. [53] H. Mannila, H. Toivonen, and A. I. Verkamo, “Discovery of frequent episodes in event sequences,” Data Mining and Knowledge Discovery, vol. 1, pp. 259–289, 1997. [54] E. Mayer, “Inductive learning of chronicles,” in European Conference on Artificial Intelligence, 1998, pp. 471–472. [55] J. McCarthy and P. J. Hayes, “Some philosophical problems from the standpoint of artificial intelligence,” Machine Intelligence, vol. 4, 1969. [56] B. Morin and H. Debar, “Correlation on intrusion: an application of chronicles,” in 6th International Conference on recent Advances in Intrusion Detection RAID, Pittsburgh, USA, september 2003. [57] K. N¨ kel, “Temporal matching: Recognizing dynamic situations from o discrete measurements,” in In Proceedings of the International Joint Conference on Artificial Intelligence IJCAI, Detroit, Michigan USA, August 1989, pp. 1255 –1260. [58] Y. Pencol and A. Subias, “A chronicle-based diagnosability approach for discrete timed-event systems: Application to web-services,” Journal of Universal Computer Science, vol. 15, no. 17, pp. 3246–3272, 2009. [59] D. F. P.M. Merlin, “Recoverability of communication protocols: Implication of a theoretical study,” IEEE Trans. Comm., vol. 24, no. 9, pp. 1036–1043, 1976. [60] D. V. Pynadath and M. P. Wellman, “Accounting for context in plan recognition, with application to traffic monitoring,” in Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, ser. UAI’95. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1995, pp. 472–481. [Online]. Available: http://dl.acm.org/citation.cfm?id=2074158.2074212 [61] D. Racoceanu, Z. Zerhouni, and N. Addouche, “Modular modeling and analysis of a distributed production system with distant specialized maintenance,” in IEEE International conference on robotics and automation, Washington DC, USA, May 2002, pp. 4046–4052. [62] N. Rota and M. Thonnat, “Activity recognition from video sequences using declarative models,” in 14th International Workshop on Principles of Diagnosis (DX00), Morelia, Michoacen, Mexico, June 2000. [63] R. Saddem, A. Toguyeni, and T. Moncef, “Consistency’s checking of chronicles’ set using time Petri nets,” in Control Automation (MED), 2010 18th Mediterranean Conference on. IEEE, 2010, pp. 1520–1525. [64] M. Sampath, R. Sengputa, S. Lafortune, K. Sinnamohideen, and D. Teneketsis, “Diagnosability of discrete-event systems,” IEEE Transactions on Automatic Control, vol. 40, pp. 1555–1575, 1995. [65] A. Subias, E. Exposito, C. Chassot, L. Trav´ -Massuy` s, and K. Drira, e e “Self-adapting strategies guided by diagnosis and situation assessment in collaborative communicating systems,” in International Workshop on Principles of Diagnosis (DX 10), Portland (USA), 2010. [66] N. VanWambeke, F. Armando, C. C. C, and E. Exposito, “A modelbased approach for self-adaptive transport protocols,” Elsevier Computer Communications, Special issue on end-to-end support over heterogeneous wired and wireless network, vol. 31(11), pp. 2699–2705, July 2008. [67] N. VanWambeke, E. Exposito, and M. Diaz, “Transport layer qos protocols: the micro-protocol approach,” in 1st OpenNet Workshop, Brussels, Belgium, March 2007. [68] A. Vautier, M.-O. Cordier, and R. Quiniou, “Extension des bases de donn´ es inductives pour la d´ couverte de chroniques,” in EGC, 2005, pp. e e 421–432. [69] J. Vizcarrondo, J. Aguilar, E. Exposito, and A. Subias, “Armiscom: Autonomic reflective middleware for management service composition,” in Global Information Infrastructure and Networking Symposium ( GIIS ), Choroni (Venezuela), december 2012. [70] J. Vizcarrondo, J. Aguilar, A. Subias, and E. Exposito, “Cronicas distribuidas para el reconocimiento de fallas en la composicin de servicios web,” in XXXIX Latin American Computing Conference (CLEI 2013), Puerto Azul(Venezuela), october 2013. [71] W. Wang, T. Guyet, R. Quiniou, M.-O. Cordier, and F. Masseglia, “Online and adaptive anomaly detection: detecting intrusions in unlabelled audit data streams,” in EGC, 2009, pp. 457–458.
  22. 22. Integration of Different Facets of Diagnosis from Control and AI L. Trav´ -Massuy` s e e CNRS, LAAS, 7, avenue du Colonel Roche, F-31400 Toulouse, France Univ of Toulouse, LAAS, F-31400 Toulouse, France Email: louise@laas.fr Abstract—Diagnosis is a rich scientific domain driven by the goal of identifying the root cause of a failure, problem, or disease from the symptoms arising from selected measurements, checks or tests. The different facets of the diagnosis problem and the wide spectrum of classes of systems make this problem interesting to several communities and call for bridging theories from different fields. This paper presents diagnosis theories proposed by the Control and the AI communities and exemplifies how they can be synergically integrated to provide better diagnostic solutions and to interactively contribute in fault management architectures. I. I NTRODUCTION The goal of diagnosis is to identify the possible causes explaining observed symptoms. A set of concomitant tasks contribute to this goal, in particular : • fault detection, which aims at discriminating normal system states from abnormal ones, i.e. states which result from the presence of a fault, • fault isolation, also called fault localization, whose goal is to point at the faulty components of the system, • fault identification, whose output is the type of fault and possibly the model of the system under this fault. In front of the diversity of systems and different views of the above problems, several scientific communities have addressed these tasks and contribute with a large spectrum of methods. The Signal Processing, Control and Artificial Intelligence (AI) communities are on the front. Diagnosis works from the signals that permit efficient fault detection towards the upper levels of supervision that call for qualitative interpretations. Signal processing provided specific contributions in the form of statistic algorithms for detecting changes in signals, hence detecting faults. This track has been surveyed in several reference books and papers [1], [2], [3], [4], [5] and remains out of the scope of this paper. Interfaces between continuous signals and their abstract interpretations, in symbolic or event-based form, implement the qualitative interpretations of the signals that are required for supervision. To do that, discrete formalisms borrowed from Artificial Intelligence find a natural link with continuous models from the Control community. These two communities have their own model-based diagnosis track : • the FDI (Fault Detection and Isolation) track, whose foundations are based on engineering disciplines, such as control theory and statistical decision making, • the DX (Diagnosis) track, whose foundations are derived from the fields of logic, combinatorial optimization, search algorithms and complexity analysis. In the last decade, there has been a growing number of researchers in both communities, who tried to understand and incorporate approaches from the FDI and DX fields to build better, more robust and effective diagnostic systems. In this paper, the concepts and results of the FDI and DX tracks are put in correspondence and the lessons learned from this comparative analysis are pointed out. Data-based diagnosis approaches based on machine learning techniques are present in both the Control and AI communities and complement model-based approaches to provide solutions to a variety of diagnostic problems where difficulty arises from the scarce nature of the instrumentation or, conversely, from the massive amounts of data to be interpreted for the emergence of hidden knowledge. Interesting bridges can be foreseen between model-based and data-based approaches and these are illustrated in this paper with the problem of learning the models that support model-based diagnosis reasoning. Other bridges can be found when considering that diagnosis is not a goal per se but a component in fault management architectures. It takes part in the solutions produced for tasks such as design, failure-mode-and-effects analysis, sensor placement, on-board recovery, condition monitoring, maintenance, repair and therapy planning, prognosis. The contribution of diagnosis in such architectures means close links with decision tasks such as control and planning and calls for innovative integrations. In this paper, different facets of diagnosis investigated in the Control or the AI fields are discussed. While [6], [7], [8] provide three interesting surveys of the different approaches that exist in these fields, this paper aims at reporting the works that integrate approaches of both sides, hence creating ”bridges”. The paper is organized as follows. After the introduction section, section II first presents a brief overview of the approaches proposed by the model-based diagnosis communities, FDI and DX, in subsections II-A and II-B, respectively. Although quite commonplace, this overview is necessary because it provides the basic concepts and principles that form the foundations of any diagnosis method. It is followed by subsection II-C that compares the concepts and techniques used by the FDI and DX communities and presents the lessons learned from this comparative analysis. Section III is concerned with the trends that integrate and take advantage of techniques from both sides, in particular causal model-based diagnosis in
  23. 23. subsection III-A and diagnosis of hybrid systems in subsection III-B. Section IV then raises the problem of obtaining the models supporting diagnosis reasoning and discusses bridges that can contribute to learning them in an automated manner. Section V widens the scope of diagnosis and is concerned with diagnosis as a component of fault management architectures, discussing several links with control and planning. Finally, section VI concludes the paper. II. The diagnosis principles are the same, although each community has developed its own concepts and methods, guided by different modelling paradigms. FDI relies on analytical models, linear algebra, and non linear system theory whereas DX takes its bases in logic formalisms. In the 2000s, catalyzed by the BRIDGE group ”Bridging AI and Control Engineering model-based diagnosis approaches ” [9] within the Network of Excellence MONET II [10] and its French counterpart, the IMALAIA group ”Int´ gration de M´ thodes Alliant Automae e tique and IA” supported by GDR MACS [11], GDR I3 [12], as well as AFIA [13], there were more and more researchers who tried to understand and synergistically integrate methods from the two tracks to propose more efficient diagnostic solutions. This collaboration resulted in the organization of several events : • BM : dx/dt = f (x(t), u(t), θ) OM : y(t) = g(x(t), u(t), θ). DX AND FDI MODEL - BASED DIAGNOSIS BRIDGE The FDI and DX streams both approach the diagnosis problem from a system point of view, hence resulting in large overlaps, including the name of the tracks: Model-Based Diagnosis (MBD). • between system inputs and outputs, i.e. the set of measurable variables Z, as well as the internal states, i.e. the set of unknown variables X. The variables z ∈ Z et the variables x ∈ X are functions of time. The typical model may be formulated in the temporal domain, then known as a statespace model of the form: a BRIDGE Workshop in 2001 in the framework of DX’01, 12th International workshop on Principles of Diagnosis, Sansicario, Via Lattea, Italy, 5-9 Mars 2001 [14], the co-location of the two main events of the FDI and the DX communities, namely the Symposium IFAC Safeprocess 2003 and the International Workshop ”Principles of Diagnosis” DX 2003, in Washington DC (USA) in June 2003 with a BRIDGE Workshop in the form of a join day. This events were followed by the publication of a special issue of the IEEE SMC Transactions, Part B, on the topic Diagnosis of Complex Systems: Bridging the methodologies of the FDI and DX Communities in 2004 by [15]. The Bridge track was launched and is still active today. Lets’s mention the two invited sessions ”AI methods for Modelbased Diagnosis” and ”Bridge between Control Theory and AI methods for Model-based Diagnosis”, recently organized in the framework of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes Safeprocess’09, Barcelona, Spain, 30 July-3 August 2009 [16]. The next subsections first summarize the foundations of the FDI and DX methods, then proceed to a comparative analysis that allows us to draw some practical assessments in the form of lessons learned. A. Brief overview of FDI approaches The detection and diagnosis methods of the FDI communauty rely on behavioral models that establish the constraints (1) where x(t) ∈ nx is the state vector, u(t) ∈ nu is the input vector and y(t) ∈ np is the output vector. BM is the behavioral model and OM is the observation model. The whole model is noted SM (z, x). The equations of SM (z, x) may be associated to components but this information is not represented explicitly. The models can also be formulated in the frequency domain (transfer functions in the linear case). Models are used in three families of methods: • the methods based on parameter estimation that focus on the value of parameters as representing physical features of the system, • the methods based on state estimation that rely on the estimation of unknown variables, • the methods based on the parity space that rely on the elimination of unknown variables. The books [17], [18], [19], [20] provide excellent surveys, which cite the original papers that the reader is encouraged to consult. The equivalence between observers, parity and paramater estimation has been proved in the linear case [21]. The concept central to FDI methods is the concept of residual and one of the main problems is to generate residuals. Let’s consider the model SM (z, x) of a system in the form (1). SM (z, x) is said to be consistent with an observed trajectory z, or simply consistent with measurements z, if there exists a trajectory of x such that the equations of SM (z, x) are satisfied. Definition 1 (Residual generator for SM (z, x)): A sys˜ tem that takes as input a sub-set of measured variables Z ⊆ Z and generates as output a scalar r,is a residual generator for the model SM (z, x) if for all z consistent with SM (z, x), we have limt→∞ r(t) = 0. When the system model is consistent with measurements, the residuals tend to zero as t tends to infinity, otherwise some residuals may be different from zero. The residuals are often optimized to be robust to disturbancies [22] and to take into account uncertainties [23]. The evaluation of residuals and assigning them a boolean value (0 or non 0) generally calls for statistical decision techniques [19]. The methods based on parameter estimation are used for linear as well as non linear systems [24]. Fault detection is achieved by comparing the estimated parameter values to their nominal values. With these methods, fault detection, isolation, and identification are achieved at once, provided that model parameters can be put in correspondence with physical

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