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Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
Embedded Service Oriented Monitoring for the Energy Aware Factory
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Embedded Service Oriented Monitoring for the Energy Aware Factory

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A KPI monitoring system has been developed to monitor the critical performance of a real factory automation testbed. …

A KPI monitoring system has been developed to monitor the critical performance of a real factory automation testbed.
Future work
Setting thresholds (targets) for KPIs
Alarm mechanism to announce interesting behaviors
Conversion of shop floor and MES information into meaningful pieces of advices

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  • In order to survive in the competitive global market, manufacturers need to make sure that their production systems are flexible, efficient. At the same time, they also need to take sustainability into consideration. European Council set out the energy 2020 strategy in March 2007. The goal is to reduce total energy use by 20%, increase renewable energy use to 20% and reduce green house gas emission by 20% by the year of 2020.
  • The first step in our solution to achieve these goals is to define key performance indicators for crucial aspects of production systems. Considering the complex nature in modern production systems, we need to use IT technologies to gather data and process them in real time to KPIs.
  • This is the model we use to develop our KPIs, this is borrowed from previous studies. The first is to define goals and objectives to all key aspects of the organization. This allows to identify potential relevant indicators. In the third step, we can select the indicators for implementation. Then we set targets for the indicators, implement the indicators, evaluate on the results and act on results. In this way, we are able to improve the production processes. In addition, unnecessary indicators should be eliminated and the introduction of new indicators should be considered. We start the process again to achieve new goals.
  • The test bed used in this paper is a manufacturing system simulating the process of cell phone manufacturing by drawing it. Each cell is composed of a conveyor system, a robot, an energy meter and pen feeders.It draws three components on a piece of paper, a frame, a screen and a keyboard. Each component has three models.
  • Each model can also have three different colours.
  • All in all, we have 729 products manufactured on our production line.
  • The production line has 12 cells. Cell 1 is the main cell, each product enters and departs the line from cell 1, cell 7 is used as a buffer. Other cells is in charge of the actual drawing processes. There are 4 embedded controllers on each cell publishing the device information as web services concerning status of the robot, the conveyor system the pen feeder and energy consumption.On cell one, we have controller related to a machine vision system publishing the inspection results.
  • Concerning the implementation of the KPI monitoring system, we shall take a look at the technologies we are using first. We use Spring web services to implement a SOAP web service to enable to communication between the production line and our monitoring system. Esper is a complex event processing component, we use it to process the raw events to KPIs. A Spring MVC framework is used to implement a web application, in which we use Hibernate as Data persistence API, the database we use is MySQL. The front-end technologies we use for visualization are HTML, jQuery and Google Charts.
  • Then, we can take a look at the Complex event processing engine. It is a rule based engine. We can edit rules to calculate our indicators, each rule is associated with a listener class. They are registered in the engine. Then we send every event to the engine in real time. A match on the rules invokes the update method in the listener class.
  • This slide shows the indicators concerning efficiency, energy, reliability and quality.In energy, we have total energy consumption on each cell, energy consumption for one pallet. We are also concerned about reliability because more reliable production line increases the efficiency and decreases the financial loss.At last, we have indicators for our inspection results. We can monitor the quality rate of each component as well as the whole product.
  • A KPI monitoring system has been developed. We can monitor crucial aspects of a factory automation testbed.Our future work will concentrate on target setting. We also plan to add alarms to each indicator to report interesting behaviours. In long terms, we plan to convert the information in numbers into meaningful advices.
  • Transcript

    • 1. Embedded Service Oriented Monitoring for the Energy Aware Factory•Date: September, 2012 Conference: 2012 IEEE International•Linked to: eSONIA Conference on Information and Automation for Sustainability Title of the paper: Embedded Service Oriented Monitoring for the Energy Aware Factory Authors: Corina Postelnicu, Bin Zhang,Contact information Jose Luis Martinez LastraTampere University of Technology,FAST Laboratory,P.O. Box 600, If you would like to receive a reprint ofFIN-33101 Tampere, the original paper, please contact usFinlandEmail: fast@tut.fiwww.tut.fi/fast Embedded Service Oriented Monitoring for the 8.10.2012 1 Energy Aware Factory
    • 2. Embedded ServiceOriented Monitoring for the Energy Aware Factory Corina Postelnicu Bin Zhang Jose L. Martinez Lastra Factory Automation Systems and Technologies Tampere University of Technology, Finland ICIAfS 2012, Beijing, China 27-29.9.2012 ARTEMIS eSONIA project (Embedded Service Oriented Monitoring, Diagnostics and Control: Towards the Asset Aware and Self Recovery Factory)
    • 3. Outline1.Introduction2.The model3.Testbed4.Implementation – Technologies – Complex Event Processing – Key Performance Indicators5.Conclusions and future work Embedded Service Oriented Monitoring for the 8.10.2012 3 Energy Aware Factory
    • 4. Introduction1.Requirements for modern manufacturing systems Flexibility, Efficiency, Etc2.Energy 2020 strategy by European Council in March 2007 1. Reduce total energy use by 20% 2. Increase renewable energy use to 20% 3. Reduce greenhouse gas emission by 20% Embedded Service Oriented Monitoring for the 8.10.2012 4 Energy Aware Factory
    • 5. IntroductionSolution 1.Define Key Performance Indicators (KPIs) 2.To gather and process data in real time to KPIs 3.To display KPIs in real-time on web Embedded Service Oriented Monitoring for the 8.10.2012 5 Energy Aware Factory
    • 6. The model Embedded Service Oriented Monitoring for the 8.10.2012 6 Energy Aware Factory
    • 7. Testbed Embedded Service Oriented Monitoring for the 8.10.2012 7 Energy Aware Factory
    • 8. Testbed Embedded Service Oriented Monitoring for the 8.10.2012 8 Energy Aware Factory
    • 9. Testbed + + + + + + + + = 729 product variants Embedded Service Oriented Monitoring for the 8.10.2012 9 Energy Aware Factory
    • 10. TestbedEmbedded controllers to publish the device information asweb servicesEach cell has 4 controllersCell 1 has an extra controller Energy consumption Embedded Service Oriented Monitoring for the 8.10.2012 10 Energy Aware Factory
    • 11. Implementation:Technologies SOAP WS Runtime KPIs Web application Data persistence Visualization Embedded Service Oriented Monitoring for the 8.10.2012 11 Energy Aware Factory
    • 12. Implementation: Complex Event ProcessingEvent Processing Language (EPL): Listener:select a.cellId as cellId, a.dateTime as dateTime, ….b.AWATTHR as energy, a.palletId as palletId public void update(Map map){from pattern [ every a=EquipmentChangeState String cellId = map.get(“cellId”).toString();(currentState="READY-PROCESSING- ….EXECUTING") -> b=EnergyMeter(cellId=a.cellId)] } Rules Listeners Event flow Esper Engine Event registration As the events flowing to the engine, a match on the EPL invokes the update method in the Listener. Embedded Service Oriented Monitoring for the 8.10.2012 12 Energy Aware Factory
    • 13. Implementation: KeyPerformance Indicators  Efficiency  Energy – Unit energy consumption • Cell energy consumption – Process energy • Unit energy consumption consumption – Unit production time – Unit processing time – Production shutdowns – Cell production rate  Reliability  Quality • Mean time-to-repair (MTTR) • Frame quality rate • Mean time-to-failure (MTTF) • Keyboard quality rate • Mean time between failure • Screen quality rate (MTBF) • Overall quality rate Embedded Service Oriented Monitoring for the 8.10.2012 14 Energy Aware Factory
    • 14. Conclusions and future workA KPI monitoring system has been developed to monitor the critical performance of a real factory automation testbed.Future work 1. Setting thresholds (targets) for KPIs 2. Alarm mechanism to announce interesting behaviors 3. Conversion of shop floor and MES information into meaningful pieces of advices Embedded Service Oriented Monitoring for the 8.10.2012 15 Energy Aware Factory
    • 15. Thank you! corina.postelnicu@tut.fi bin.zhang@tut.fiARTEMIS eSONIA project (Embedded Service Oriented Monitoring, Diagnostics and Control: Towards the Asset Aware and Self Recovery Factory) Embedded Service Oriented Monitoring for the 8.10.2012 16 Energy Aware Factory

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