NASA Intelligent Self-Situational Awareness Project
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  • Developing validated models and data fusion knowledge bases for CBM requires: Normal, seeded, and transitional data Well documented, ground truth data Prior to MURI IPD: Lack of existing, high-fidelity data sets pertaining to faults initiation and evolution Need for realistic data sets to develop CBM technology (e.g., failure models, diagnostic algorithms, predictive techniques, data fusion, reasoning) As a result of MURI IPD: Data sets are made available through cooperative arrangements with other research and developers MDTB benchmark data sets for are used as Measures of Effectiveness/ Performance for CBM algorithms
  • CMMS = Computerized Maintenance Management System LCMS = Life-Cycle Management System MC = Maintenance Controller SME = Subject Matter Expert ISEA = In-Service Engineering Agent PM = Program Manager GCSS = Global Combat Support System OPS = Operations
  • Slide was taken from an PM AL presentation to depict basic software fuctions and information exchanges required to support future AL expected capabilities on system. Capabilities document only functions in support of platform health monitoring and reporting. Software, data and implied harware functions/capabilities listed do not necessarily reflect capability which will be provided by AL however atleast some portion would be required to support AL to work on the platform.
  • Distributed Processing is further define in the Off platform portion of the architecture Distributed Processing Capability – Platform Data is Distributed & Shared Throughout The Brigade and Higher As Required Maintenance Data & Status to the Combat Repair Team & Forward Maintenance Company Log data (Fuel, Ammo, & Personnel) to the Combat Trains and Brigade Support Area Distributed Data Processing – Off Platform processes include The current Unit Level Logistics System automatically receives and processes platform data to add fault and order parts Decision Support Tools (PMM) provides CBM & Fleet Status National Level data stored in PMM until Data Hub is in place Reach as Required Capability - Has two functional requirements Drill Down Capability for Combatant Commanders and Logisticians to obtain additional information Remote Diagnostics provides a reach back capability to perform troubleshooting from the FMC or CRT to the platform Future Plans – Linked to ERP Data Hub and WIN-T
  • Distributed Processing is further define in the Off platform portion of the architecture Distributed Processing Capability – Platform Data is Distributed & Shared Throughout The Brigade and Higher As Required Maintenance Data & Status to the Combat Repair Team & Forward Maintenance Company Log data (Fuel, Ammo, & Personnel) to the Combat Trains and Brigade Support Area Distributed Data Processing – Off Platform processes include The current Unit Level Logistics System automatically receives and processes platform data to add fault and order parts Decision Support Tools (PMM) provides CBM & Fleet Status National Level data stored in PMM until Data Hub is in place Reach as Required Capability - Has two functional requirements Drill Down Capability for Combatant Commanders and Logisticians to obtain additional information Remote Diagnostics provides a reach back capability to perform troubleshooting from the FMC or CRT to the platform Future Plans – Linked to ERP Data Hub and WIN-T

NASA Intelligent Self-Situational Awareness Project NASA Intelligent Self-Situational Awareness Project Presentation Transcript

  • Applications of Data Mining in Automated ISHM and Control for Complex Engineering Systems Karl M. Reichard Applied Research Laboratory Penn State University Voice: 814-863-7681 Email: [email_address]
  • Data Mining
    • Data mining , or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data.
    • Data mining tools are used to identify correlations and to predict behaviors and future trends from data.
    • Businesses use data mining to search and analyze databases for hidden patterns and may find trends and predictive information that experts may miss because it lies outside their expectations or because they never thought to look for correlations there.
  • Data Mining
    • Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for valuable minerals.
    • For businesses, the economics of data mining have many analogies to traditional mineral mining – pay for access to public data and make money from mining it.
    • A data warehouse is a repository where data located in disparate databases are consolidated. Data warehouses store large quantities of data by specific categories so users can easily retrieve, interpret and sort the contents.
  • Elements of Data Mining
    • Data preparation
    • Databases and data warehouses
    • Machine learning
      • Statistical models
      • Clustering
    • Data Models
    • Knowledge Representation
      • Association Rules
      • Classification Rules
      • Decision trees
    • Data transformation
    • Empirical Science
      • Scientist gathers data by direct observation
      • Scientist analyzes data
    • Analytical Science
      • Scientist builds analytical model
      • Makes predictions.
    • Computational Science
      • Simulate analytical model
      • Validate model and makes predictions
    • Science - Informatics
      • Data captured by instruments Or data generated by simulator
      • Processed by software
      • Placed in a database / files
      • Scientist analyzes database / files
    Data Mining as Scientific Evolution Jim Gray, “Online Science the New Computational Science,” Microsoft Research.
  • Leveraging Technology Trends
    • Moore’s Law
      • Performance/Price doubles every 18 months
      • 100x per decade
    • Metcalfe’s law
      • Utility of computer networks grows as the number of possible connections: O(N 2 )
    • Computer storage capacity is actually beating Moore’s law
    Jim Gray, “Online Science the New Computational Science,” Microsoft Research.
    • Premise: Most data is (or could be online) so, the Internet is the world’s best telescope:
      • It has data on every part of the sky, in every measured spectral band: optical, x-ray, radio..
      • It is up when you are up. The “seeing” is always great (no working at night, no clouds no moons no..).
      • It’s a smart telescope: links objects and data to literature on them.
    Example: World Wide Telescope Virtual Observatory http://www.us-vo.org / http:// www.ivoa.net / Jim Gray, “Online Science the New Computational Science,” Microsoft Research.
  • Why are Computer Scientists interested in Astronomy Data?
    • It has no commercial value
      • No privacy concerns
      • Can freely share results with others
      • Great for experimenting with algorithms
    • It is real and well documented
      • High-dimensional data (with confidence intervals)
      • Spatial data
      • Temporal data
    • Many different instruments from many different places and many different times
    • Federation is a goal
    • There is a lot of it (petabytes)
    Jim Gray, “Online Science the New Computational Science,” Microsoft Research. IRAS 100  ROSAT ~keV DSS Optical 2MASS 2  IRAS 25  NVSS 20cm WENSS 92cm GB 6cm
  • ISHM Development and Implementation Collect raw data Perform assessment Make diagnosis Reduce data Implementation Analyze data Sensing Feature Extraction Fault Detection Damage Estimation Feature Tracking & Prognostics Development Sensor Selection Develop Physical Models Transitional Failure Data Diagnostic Prognostic Algorithms Systems Interfaces Open Systems Architecture Degrader Analysis Where can we apply data mining techniques in ISHM development and implementation?
  • Data Mining in ISHM Development Sensor Selection Develop Physical Models Transitional Failure Data Diagnostic Prognostic Algorithms Systems Interfaces Degrader Analysis Parts usage Repair orders Maint. logs
    • Initial design and development
    • ISHM system verification and validation
    • Continuous ISHM system improvement (borrowing techniques from manufacturing and quality control)
    Sensor reliability Operational data ISHM system data Maint. logs Biggest payoff may be in collection and analysis of transitional failure data across large numbers systems
  • Data Mining in Prognostics RCM Analysis Detection & Diagnostic Algorithms Prognostics Predicted Remaining Useful Life Anticipated Demand
    • Transitional failure data from seeded fault experiments
    • Transitional data from analogous systems
    • Operational data
    Load history Transformation Model Data mining may allow access to much larger sets of transitional data for assessing fault severity and predicting remaining useful life Sensors Physical Models
  • Managing System Health Information Platform Data Store Platform Data Store Platform Data Store Platform Data Store Platform Data Store Platform Data Store Platform Data Store Platform Data Store Regional Data Store (CRIS) Regional Analysis Net-Centric Compliant Service OSA-EAI Tech-XML Global Data Store (CRIS) 3 rd Party Analysis Net-Centric Compliant Service OSA-EAI or COTS Replication Depot Analysis Net Centric Attributes Only handle information once Tag and post before processing Store publicly and advertise Register metadata Tag applications for discovery Platform Data Store Platform Data Store Platform Data Store Platform Data Store Platform Data Store Platform Data Store Platform Data Store Platform Data Store Regional Data Store (CRIS) Regional Analysis Net-Centric Compliant Service OSA-EAI Tech-XML OEM Analysis Database Flat file or XML Database Flat file or XML PM IDEs What types of data are we talking about? Asset Register Management Work Management Diagnostic / Health / Rec (?) Static Trends / Alarms Dynamic Vibration / Sound Oil /Fluid / Gas / Solid Tests Binary Data / Thermography Reliability Data
  • Leveraging Platform Asset Health Information SME Supply Smart Maintainer Maintenance Life Cycle Mgmt PM MC What is the status of all my platform Cs? What is broken or about to break on this vehicle? I need to report a problem. Give me battery health data for all of platform type As. What are the new maintenance requests? Do I have the capacity? Operations Logistician Planner Do I have assets required for the upcoming mission? What risks do I mitigate or introduce? When do I need to buy material for systems that are predicted to fail? Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 2 Vehicle Type A Block 2 Vehicle Type A Block 2 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type B Block 2 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 1 Vehicle Type A Block 2 Vehicle Type A Block 2 Vehicle Type C Block 2 Vehicle Type B Block 1 Vehicle Type C Block 1 Operator How can I automatically report when the remaining useful life of an LRU drops below threshold? Integrated Architecture Transfer
  • USMC Autonomic Logistics Architecture Internal Message Manager GCCS GCSS AL Operator / Maintainer Presentation & Interface EXternal Message Manager Message Manager Adapters Platform Level e Book Configuration BIT/BITE Sensor Maintenance Operational Embedded e Data Tech Data Fault Tree RPSTL e PMCS Serial UID/AIT Configuration Remote Diag. Data Sync Remote Diagnostics Alerts Trending Algorithms HUMS Data Download Manager Platform LRU LRU LRU LRU LRU LRU LRU
  • US Army Common Logistics Operating Environment
  • US Army Common Logistics Operating Environment Maintain on-platform persistent health data store using MIMOSA OSA-EAI CRIS or Tech-XML Bulk health data transfer using MIMOSA OSA-EAI Tech-XML Manage off- platform persistent health data store using MIMOSA OSA-EAI CRIS Emergency Resupply Request using JVMF K07.xx messages Replicate to CONUS using COTS tools Query persistent health data store using MIMOSA OSA-EAI Tech-XML
  • CACE - Coherent Analytical Computing Environment CACE is an example of a data mining application which taps into databases of maintenance requests, repair schedules, and training schedules to optimize flight operations
  • Access to Data
    • Need access to data for all information customers
      • Embedded ISHM systems
      • Operators
      • Maintainers
      • Planners
      • Program managers
      • Design Engineers
    • Latency of information will determine value to different customers
    • Who is responsible for maintaining the data warehouse?
  • Open Systems Architecture for Condition Based Maintenance
    • Standardized architecture for health and condition monitoring systems
    • Breaks monitoring system into functional layers
    • Defines inputs and outputs required for each layer
    • Modules not confined to one locale
    • Middleware independent
    Uncertain Healthy Faulty Advisory Generation Prognostic Assessment Diagnostic Health Assessment Presentation Alarm & Event State Detection Envelope Detector Band-Pass Filter Half-wave or Full-wave Rectifier Peak-Hold Smoothing Accelerometer Data Band-Pass Filtered Signal Rectified Signal Envelope Detected Signal     2 1 1 2 1 4 1 4                      m j N i j ij N i i r r m r r N NA Data Manipulation Smart Sensor Distributed Computing Technology Data Acquisition Sensor
  • Standards for Enabling System Management Machinery Information Management Open Standards Alliance (MIMOSA) MIMOSA Technology Types REG (Physical Asset Register Management) WORK (O&M Agent Work Management) DIAG (Diagnostics / Prognostics / Health Assessment) TREND (Operational Scalar Data & Alarms) DYN (Dynamic Vibration/Sound Data & Alarms) SAMPLE (Oil/Fluid/Gas/Solid Test Data & Alarms) BLOB (Binary Data/Thermography Data & Alarms) REL (RCM/FMECA/Model Reliability Information) ALGORITHM (Algorithm Management Information) AGENT (Intelligent Agent Management Information) TRACK (Physical Asset GeoSpatial Tracking Info.) FORECAST (Capability Forecasting & Projections)
  • Beyond System Health Monitoring Need techniques to capture decisions and actions in addition to data and permit machines to mine those high level control “lessons learned” Intelligent Monitoring Fault Detection Safety Fault Location & Severity Maintenance/ Repair Diagnosis & Remaining Life Condition- Based Maintenance Prognosis Health Management Intelligent Control + Relating Subsystem Health Intelligent Systems Management Vehicle Monitoring & Control Vehicle Management Intelligent “ System” Automation Mission Management
  • Other Data Mining Challenges
    • Data management
    • Data storage
    • Indexing data for efficient computer access
    • Ownership of data, data rights, and security
    • Distributed processing architectures
    • Data summarization, trend detection anomaly detection are key technologies
    • Translation models
  • Conclusions
    • Data mining is the search for hidden relationships and meaning in data
    • Applications in ISHM include modeling, diagnostics, and prognostics
    • Useful for system development and for closed-loop improvement of systems
    • Biggest payoff could be in creation of virtual transitional failure data sets
    • Information customers are driving for network-centric applications so databases and data warehouses will be there – we need to be sure the data is useful for ISHM