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