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Michael Elsass (Ohio State University), Saravanarajan (UCLA), James F. Davis (UCLA),

Information Management in an Integrated Decision Support Framework for Process Fault Detection and Diagnosis Early Event Detection and Diagnostic Localization. Michael Elsass (Ohio State University), Saravanarajan (UCLA), James F. Davis (UCLA),

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Michael Elsass (Ohio State University), Saravanarajan (UCLA), James F. Davis (UCLA),

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  1. Information Management in an Integrated Decision Support Framework for Process Fault Detection and Diagnosis Early Event Detection and Diagnostic Localization Michael Elsass (Ohio State University), Saravanarajan (UCLA), James F. Davis (UCLA), Dinkar Mylaraswamy (Honeywell Laboratories), Dal Vernon Reising (Honeywell Laboratories) and John Josephson (Ohio State University)  Sponsored by Abnormal Situation Management® Consortium November 7, 2002

  2. ASM Abnormal Situation Management® Joint Research and Development Consortium Creating anew paradigm for operationof complex industrial plants, withsolution conceptsthat improve Operations’ ability toprevent and respond to abnormal situations. www.asmconsortium.com

  3. UNEXPECTED EVENTS COST 3-8% OF CAPACITY$10 Billion annually in lost production in US Petrochemical (Plus equipment repair/replacement & human costs) Source: ASM® Consortium Research Plant Operating Target Planning Constraints Plant Availability Operational Constraints Days per Year Optimization efforts Plant Incidents Plant Capacity Limit < 60% 95% 100% Daily Production Level

  4. ASM® Consortium Research Projects • Alarm System Performance Metrics • Human Performance Model for Alarm Response • Procedural Operations • Mobile Devices in Field Operations • State Estimation for Early Event Detection

  5. EED Objectives Detection and Rapid Functional Assessment Operator Drill Down Diagnostic Localization Localize Fault and Failures EED

  6. Case Study: Demethanizer • ~100 sensors • 1 reading per minute • Data annotated for abnormal events Tested with blind cases

  7. 90 85 80 75 70 65 60 55 50 45 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 4 x 10 Typical Annotated Abnormal Event Abnormal Event data Condenser Level Normal Data End Start

  8. Rapid Assessment and Operator Drill Down Detection Rapid assimilation of functional anomaly Function-driven trend plots Triggers diagnostic localization

  9. Operator GUI Polar Star Condenser Function Reboiler Function Ergonomically successful Meets ASM UI guidelines Effective part of operator enviornment Heat Sink Function Heat Source Function Heat Transfer Function Cond Lvl Function Detection and Functional Assessment: Function Driven Trend Plots

  10. Functional Hierarchy & Distributed State Estimation Demethanizer Separation Maintain Bottoms composition Maintain column pressure Maintain enough VL equilibrium Maintain Overhead composition Maintain material balance Real-world effectiveness PCA ART QTA SPC First Principles Functional sensor groups & Numeric-symbolic mapping

  11. Diagnostic Localization Aggregate evidence Assess variables and processes Localize possible sources Distinguish sources from effects

  12. Functional Representation Device - system/equipment Mode categorizes failure, fault and normal Function/ Malfunction Device Function (malfunction) organizes CPDs CPD Inlet/outlet states characterize device state Behavior State conditions at a device port

  13. Device Centric for Reusability Knowledge is organized into a device library Library devices are connected to reflect process topology

  14. F Causal Link Assessment Algorithm Every device, every time step Device states are accumulated at each step to generate a process state Static view at given time step Branching managed: data, simple devices to constrain complex, device order Not propagation Pump high-flow Valve high flow low signal Valve high flow high signal Sensor high flow high signal Sensor high flow low signal Sensor high flow high signal Sensor high flow low signal Sensor high flow normal signal

  15. Process State Generation Blue: 1 malfunctions Green: 2 malfunctions Red: 3 malfunctions Each row corresponds To a device Each process state is unique Represents a device behavior Temp. sensor: normal temp high signal

  16. Ranking Hypotheses Single vs multiple malfunctioning components Persistence across time steps Comparison with state estimators Example Hypotheses

  17. Detection & Rapid Assessment GUI

  18. Conclusions EED Integrated Operational View • Plant • Operator • Decision support Integrated Functionality • Rapid functional assessment • Operator drill down • Diagnostic localization Integrated Approach • Function driven detection • Function driven trend plots • Causal link assessment

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