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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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

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

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.

Questions and Comments?

Nancy Lybeck

Idaho National Laboratory

Nancy.Lybeck@inl.gov

208-526-1033