an assessment of integrated health management frameworks
TRANSCRIPT
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An Assessment of Integrated Health Management Frameworks
Nancy J. Lybeck and Magdy S. Tawfik Idaho National Laboratory
&
Leonard J. Bond and Jamie Coble
Pacific Northwest National Laboratory
May 14, 2012
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Presentation Outline and Goals
Outline
1. Functions of a full PHM system
2. Desirable features of an Integrated Health Management (IHM) Architecture
3. Existing and emerging standards
4. Examples of commercially available architectures
Goal: To provoke a thoughtful approach to selection of IHM architectures for use in nuclear applications
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How do we implement PHM?
• Many separate functions must work together
• Need an overarching software application – an integrated health management architecture (framework)
• Prefer commercially available product
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Considerations for a COTS Architecture
1. Open, modular architecture
– Independent of diagnostic and prognostic algorithms
– Well-published interfaces
– Promotes competition
2. Platform Independence
– Not tied to any single computer platform
– Not tied to any single operating system
3. Web-based tool set
– System flexibility
– Ready access to information over a computer network
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Considerations for a COTS Architecture, Cont.
4. Graphical user interface
– Can easily overwhelm the control room staff with the amount of data available to them
– Well-designed GUI makes sure critical information is not missed
5. Scalability
– Must scale to the size of a NPP
6. Compatibility with existing or emerging standards and specifications
– ISO 13374
– ISO 18435
– SAE AIR5871
– MIMOSA OSA-CBM
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Prognostic Health Management System
• ISO 13374 is a collection of standards that define a general condition monitoring architecture (framework) for machines
• Six Functional Blocks in a Condition Monitoring System (ISO 13374)
– Data Acquisition
– Data Manipulation
– State Detection
– Health Assessment
– Prognostic Assessment
– Advisory Generation
• MIMOSA’s OSA-CBM is an implementation of ISO 13374
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Other Applicable Standards
• ISO 13381 provides general guidelines for the development of machinery prognostics
• ISO 18435 describes and integration model and interfaces to facilitate integration of Condition Based Maintenance information with operating and environmental information
• SAE AIR 5871 provides terminology and guidelines for the application of prognostics to gas turbine engines
• MIMOSA OSA-EAI defines a data repository for asset management
• Diag-ML is a fully extensible schema developed in Extensible Markup Language (XML) that defines a format for transferring diagnostic information.
• Additional standards are under development by groups such as the IEEE Reliability Society
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Prognostic Software
Company Product
Optimized Systems and Solutions Inc. (OSyS) AOC and EHM
DSI International eXpress
Scientech FAMOS
IMS Watchdog Agent
PHM Technology MADe
IBM Maximo
University of Strathclyde Multi-Agent Systems
Matrikon Operational Insight and
Equipment Condition Monitor
ESRG OstiaEdge
University of Tennessee PEP and PEM
SmartSignal PlantAPS
Impact Technologies SignalPro and ReasonPro
Expert Microsystems SureSense
AMS Corporation OLM
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Research-Oriented Software
• University of Tennessee: PEP & PEM
– MATLAB toolboxes
– PEP supports fast prototyping of prognostic algorithms
– PEM provides functionality for automated system monitoring and fault detection
• University of Cincinnati Center for Intelligent Maintenance Systems: Watchdog Agent
– Supports fast prototyping of algorithms
– Deployed with LabView (National Instruments)
– Not expected to scale well to very large, complex applications
• University of Strathclyde: Multi-Agent Systems
– Promising approach to information fusion for health monitoring
– Inherently extensible and flexible
– No commercially available products were identified
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PHM System Development Tools
• PHM Development tools
– Design assessment and optimization for the purpose of fault detection and diagnostics
– Assessment of suitability for fault detection and diagnostics
• Two products are purely PHM development tools:
– PHM Technology: MADe
– Impact Technologies: PHM Design
• DSI International: eXpress
– Diagnostic capability assessment
– Online diagnostics
– Based on a model of the system or plant developed in a CAD-like GUI
– Standard-compliant export via DiagML
– Currently no prognostic assessment
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Enterprise Asset Management Software
• IBM: Maximo
• Supports management of all types of business assets:
– Service ̶ Contract
– Materials ̶ Procurement
– Physical asset ̶ Work Management
• Basic data acquisition capabilities
• Signal Thresholding
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Matrikon: Operational Insight & Equipment Condition Monitor
• Based on Maximo
• Built on OpenO&M standard, which combines
– OPC data communication standards
– MIMOSA compatibility standards
• Operational Insight:
– web-based data visualization
– Key Performance Indicator (KPI) dashboard
• Equipment Condition Monitor
– equipment health monitoring
– Prioritizing O&M requirements
• Matrikon has substantial history with the power industry
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OSyS: Asset Optimization Center (AOC) & Equipment Health Management (EHM)
• Hundreds of standard interfaces to online data
• Manual data entry points
• Train and evaluate empirical models of normal system behavior
• Customers can view, analyze, or extract any data
• 3rd party models can easily be imported
• Communicates with any OPC enabled DAQ
• Drag-and-drop model-based approach to diagnostics
• Fusion of multiple diagnostic techniques
• Includes proven solutions for pumps, pipes, and pressure vessel monitoring
• Results of data analysis are displayed in a collaboration portal
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Impact Technologies: SignalPro & ReasonPro
• SignalPro
– Anomaly detection system
– Data-driven system modeling engine
– Seeks subtle changes in system behavior
• ReasonPro
– Model-based diagnostic and prognostic reasoning
– Provides robust fault isolation and identification
– Fault propagation based on associated PHM monitor sequences
– Online, offline, and mobile applications
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Scientech - FAMOS
• Six core modules
• PEPSE (power plant thermal modeling, design, and performance analysis) is already implemented in ALL U.S. NPPs.
• PMAX (thermal performance monitoring, analysis, and optimization) is used in 59 U.S. NPPs.
• CMAX – condition monitoring and diagnostics (online and offline data)
• PdP – additional fault detection engine
• R*TIME – data acquisition and historian
• Rules Engine – diagnosis and decision support
• No prognostic assessment
• Some support for integrating third party applications
• Designed to scale to a complex system such as an NPP
• Can accommodate fleet monitoring
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ESRG - OstiaEdge
• Plant Edition monitors a single plant or system
• Central Edition monitors a fleet
• Online and offline data sources
• Runs on Windows or Linux
• Web-based results portal
– Run hours
– Alarms
– Trends
– Events
• No Advisory Generation
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SmartSignal - PlantAPS
• Monitoring using Multivariate State Estimation Technique (MSET)
• Diagnose faults by fault patterns, operating data and information
• Assign fault priorities
• Currently able to detect faults in
– Pumps (boiler feed pumps and generic)
– Condensers
– Cooling water circuits
– Generators
– Steam turbines
• Managed and maintained by SmartSignal on their servers
– Web-based communication of impending plant faults and failures
– Email notification of plant personnel
• No Advisory Generation
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Expert Microsystems - SureSense
• Highly customizable java-based architecture
• Supports modular plug-ins of any 3rd party models
• Compliance to standards can be accomplished via plug-ins
• Scales to a full NPP
• Included fault detection algorithms include thresholding, range monitoring, noise estimation, derivative tests, the sequential probability ratio test, and adaptive sequential probability test
• Fault diagnosis via Bayesian Belief Network
• Drag-and-drop interface to fault diagnosis module
• No Advisory Generation
• Has been used for sensor calibration in some U.S. NPPs (TVA, SC Electric & Gas, Excelon Energy)
• Pilot study monitoring tendon slips and breaks (Ginna NPP)
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AMS: On-line Monitoring (OLM) • Developed specifically for use in nuclear power plants under AMS’
10CFR50 Appendix B Software QA program
• Integrates static and dynamic analysis under common software framework
• Static Analysis
– On-Line Transmitter Calibration Monitoring
– RTD & Thermocouple Cross Calibration
– Equipment Condition Assessment
• Dynamic Analysis
– Dynamic performance of process instrumentation
– Core flow anomaly detection and diagnostics
– Reactor internal vibration monitoring
• Web-based user interface
• Incorporates empirical modeling, redundant sensor, and noise analysis algorithms
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Conclusions
• There are many commercially available Prognostic software products
• Each has its own strengths and weaknesses
• Choice of a product to deploy requires a thoughtful assessment
• All assessments were made based on product brochures, web site information, and discussions with vendors
• This authors are not recommending any individual product for use in NPPs
• Exclusion of any available product is purely an oversight
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Acknowledgements
Funded by the U.S. Department of Energy
Under DOE Idaho Operations Office
Contract DE-AC07-05ID14517
Thanks to Pradeep Ramuhalli and Ryan Meyer at PNNL; Vivek Agarwal and Binh Pham at INL.
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Questions and Comments?
Nancy Lybeck
Idaho National Laboratory
208-526-1033