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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), - PowerPoint PPT Presentation
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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 byAbnormal Situation Management® Consortium
November 7, 2002
Information Management in an
Integrated Decision Support Framework for
Process Fault Detection and Diagnosis
Early Event Detection and Diagnostic Localization
Creating a new paradigm for operation of complex industrial plants, with solution concepts that improve Operations’ ability to prevent and respond to abnormal situations.
ASM
Abnormal Situation Management®
Joint Research and Development Consortium
www.asmconsortium.com
Plant Operating Target
Plant Capacity Limit
Daily Production Level
Day
s pe
r Yea
r
Optimization efforts
Operational Constraints
Planning Constraints
95% 100%< 60%
Plant Availability
Plant Incidents
Source: ASM® Consortium Research
UNEXPECTED EVENTS COST 3-8% OF CAPACITY $10 Billion annually in lost production in US Petrochemical
(Plus equipment repair/replacement & human costs)
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
EED ObjectivesDetection and Rapid Functional Assessment
DiagnosticLocalization
EED
LocalizeFault and Failures
OperatorDrill Down
Case Study: Demethanizer
• ~100 sensors• 1 reading per minute• Data annotated for abnormal events
Tested with blind cases
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 x 104
45
50
55
60
65
70
75
80
85
90
Normal Data
Typical Annotated Abnormal Event
Abnormal Event dataCondenser Level
Start End
Rapid Assessment and Operator Drill Down
Detection
Rapid assimilation of functional anomaly
Function-driven trend plots
Triggers diagnostic localization
Operator GUI
CondenserFunction
Heat SinkFunction
Heat SourceFunction
ReboilerFunction
Cond LvlFunction
Heat TransferFunction
Polar Star
Detection andFunctional Assessment:
FunctionDrivenTrendPlots
•Ergonomically successful
•Meets ASM UI guidelines
•Effective part of operator enviornment
Functional Hierarchy & Distributed State Estimation
Demethanizer Separation
Maintain Overhead composition
Maintain Bottoms composition
Maintain columnpressure
Maintain enough VL equilibrium
Maintain material balance
Functionalsensor groups &
Numeric-symbolicmapping
Real-world effectivenessPCAARTQTASPCFirst Principles
Diagnostic Localization
Aggregate evidence
Assess variables and processes
Localize possible sources
Distinguish sources from effects
State conditions
at a device port
CPDInlet/outlet states
characterizedevice state
Modecategorizes
failure,fault and normal
Functional RepresentationDevice - system/equipment
Function(malfunction)
organizes CPDs
Function/Malfunction
Behavior
Device
Knowledge is organized into a device library
Device Centric for Reusability
Library devices are connectedto reflect process topology
Causal Link Assessment Algorithm
Pump high-flow
Valvehigh flow
high signal
Valvehigh flowlow signal
Sensorhigh flow
high signal
Sensor high flow
normal signal
Sensorhigh flowlow signal
Sensorhigh flow
high signal
Sensorhigh flowlow signal
• 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
F
Process State Generation
Blue: 1 malfunctionsGreen: 2 malfunctionsRed: 3 malfunctions
Each process state is unique
Represents adevice behavior
Temp. sensor: normal temp high signal
Each row correspondsTo a device
Ranking Hypotheses
Single vs multiple malfunctioning components
Persistence across time steps
Comparison with state estimators
Example Hypotheses
Detection &Rapid AssessmentGUI
Conclusions EEDIntegrated 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