NSF I-UCRC on Intelligent Maintenance Systems;Planning Workshop at the University of Texas;May 15, 2012, Austin, TX
IMMUNITY INSPIRED DIAGNOSTICSAND PROGNOSTICS IN SYSTEMSOF INTERACTING DYNAMICSUBSYSTEMSPROF. DRAGAN DJURDJANOVIC
NSF I-UCRC on Intelligent Maintenance Sys.Planning Workshop at the University of Texas
2 PRESENTATION OUTLINE
»Motivation»Methodology»Preliminary Work»Research Plan
Time
Each dot is a FOUPof wafers
Particle failures – tooldown for several weeks
MOTIVATION:SEVERAL MONTHS OF PECVD TOOLOPERATION
3
Coulomb crystals
and particle failures
Again tool down forseveral weeks
4 PRESENTATION OUTLINE
»Motivation»Methodology»Preliminary Work»Research Plan
5 FAULT ISOLATION VIA DISTRIBUTEDANOMALY DETECTION
ADA4
AD3
AD2AD1
ChargeAir Cooler
EGRValve
VNT-Turbine
EGRCooler
TCACα
IntakeManifold
Pm
ExhaustManifold
λNeng
Compressor
WHFMEngineControl
Unit (ECU)
ThrottleValve Engine
XVNT
WFUEL
XEGR
Xth
*ADA -AnomalyDetector
Neng
Block diagram of the mass airflow in a diesel engine providedby the Bosch Research Center, Pittsburgh
6
ADA4
AD3
AD2AD11
ChargeAir Cooler
EGRValve
VNT-Turbine
EGRCooler
TCACα
IntakeManifold
Pm
ExhaustManifold
λNeng
Compressor
WHFMEngineControl
Unit (ECU)
ThrottleValve Engine
XVNT
WFUEL
XEGR
Xth
Neng
AD13AD12
Negative selection in natural immunesystems (Dasgupta, 2006)
*ADA -AnomalyDetector
Block diagram of the mass airflow in a diesel engine providedby the Bosch Research Center, Pittsburgh
FAULT ISOLATION VIA DISTRIBUTEDANOMALY DETECTION
7 PRESENTATION OUTLINE
»Motivation»Methodology»Preliminary Work»Research Plan
AnomalousBehavior
Compute CV
Currentbehavior
Normalbehavior
Plant
Operating Region
Localmodel 1
Localmodel 2
Localmodel 4
Localmodel 5
Localmodel 6
Localmodel 7
Piecewise Dyn. Model
+-
Anomaly Detection usingPiecewise Dynamic Models
8
J. Liu, D. Djurdjanovic, K. Marko and J. Ni, “Growing StructureMultiple Model System for Anomaly Detection and Fault Diagnosis”,Transactions of ASME, Journal of Dynamic Systems,Measurements and Control, Vol. 131, No. 5, pp. 051001-1 –051001-13, 2009
EGRCooler
Injected FuelEngine Speed
++
PI Controller−+
EGR Valve
φECULook-upTable
Exhaust Temp
Exhaust PressureIntake Man. Pressure
Ambient Temperature
Look-upTable
ADoverall
Precedent Free Fault Isolation in the Exhaust GasRecirculation (EGR) System of a Diesel Engine
TESISDYNAware, 2006
M. Cholette and D. Djurdjanovic, “Precedent-Free Fault Isolation in a Diesel EngineEGR System”, to appear in ASME J. of Dynamic Systems Measurements & Control2011
9
EGRCooler
Injected FuelEngine Speed
++
PI Controller−+
EGR Valve
φECULook-upTable
Exhaust Temp
Exhaust PressureIntake Man. Pressure
Ambient Temperature
Look-upTable
ADmassflow
ADcooler
TESISDYNAware, 2006
Precedent Free Fault Isolation in the Exhaust GasRecirculation (EGR) System of a Diesel Engine
10M. Cholette and D. Djurdjanovic, “Precedent-Free Fault Isolation in a Diesel EngineEGR System”, to appear in ASME J. of Dynamic Systems Measurements & Control2011
EGRCooler
Injected FuelEngine Speed
++
PI Controller−+
EGR Valve
φECULook-upTable
Exhaust Temp
Exhaust PressureIntake Man. Pressure
Ambient Temperature
Look-upTable
AD3
AD2 AD4 AD5
ADcooler
TESISDYNAware, 2006
Precedent Free Fault Isolation in the Exhaust GasRecirculation (EGR) System of a Diesel Engine
11M. Cholette and D. Djurdjanovic, “Precedent-Free Fault Isolation in a Diesel EngineEGR System”, to appear in ASME J. of Dynamic Systems Measurements & Control2011
EGRCooler
Injected FuelEngine Speed
++
PI Controller−+
EGR Valve
φECULook-upTable
Exhaust Temp
Exhaust PressureIntake Man. Pressure
Ambient Temperature
Look-upTable
TESISDYNAware, 2006
Anomalies in the EGR Valve(Clogging of the Valve)
12M. Cholette and D. Djurdjanovic, “Precedent-Free Fault Isolation in a Diesel EngineEGR System”, to appear in ASME J. of Dynamic Systems Measurements & Control2011
ADcooler
13
ADmassflow
AD4
AD2(Look up table)
AD3(Look up table)
AD4(PI Controller)
AD5(Valve)
Detection and Isolation of an Anomaly in the EGR Valve
14 PRESENTATION OUTLINE
»Motivation»Methodology»Preliminary Work»Research Plan
15 TASK 1: DISTRIBUTED ANOMALYDETECTION WHEN CAUSAL CONNECTIONSARE NOT CLEAR
• Physics basedreasoning and expertknowledge• Bayesian causationdiscovery approaches
NSF I-UCRC on Intelligent Maintenance Sys. Planning Workshop at the University of Texas
17 FAULT ISOLATION VIA DISTRIBUTED ANOMALY DETECTI ON
ADA 4
NSF I-UCRC on Intelligent Maintenance Sys. Planning Workshop at the University of Texas
16 TASK 2: POLICY FOR DISTRIBUTING ANOMALYDETECTORS IN A COMPLEX SYSTEM
16
d0 – Detection ratefor groupedanomaly detector
d1 – Detection ratefor individual FRUanomaly detectors CC – Computational cost per
unit time Ci – Cost of failing to localizethe fault per unit time
17 TASK 3: SIGNATURE PREDICTION FORMAINTENANCE DECISION-MAKING
• Existing methods are eitherdealing with stationary time-seriesor are too slow to provide real-timeinformation• Combine analytics withsimulation based tools to obtainaccurate predictions fast
NSF I-UCRC on Intelligent Maintenance Sys. Planning Workshop at the University of Texas
20 TASK 1: UNDERSTANDING UNCERTAINTY OF HIDDEN MARK OV MODEL ESTIM ATIO N
Similarity basedprediction
methods forpredictingsignature
distributions
Warning about adegraded state
Root cause of thedegraded state
18
POTENTIAL APPLICATION AREAS
• Automotive engines• Aircraft jet engines• Advanced
manufacturingequipment
• Electrical generatorsystems
• Modular data centers
SUMMARY OF PROPOSED WORK19
Task 1: Identification of causalrelations between subsystems in acomplex system
Task 2: Optimization of policies fordistribution of anomaly detectorsthat achieve tradeoff betweenspeed and accuracy of faultlocalization and computation load
Task 3: Implementation ofdegradation monitoring techniquesto a real system
• Mentors: Prof. DraganDjurdjanovic & industrialpartners
• Researchers: One graduatestudent per application areain year 1 and 2 graduatestudents in year 2 (possibleapplication areas includemanufacturing equipment,engines, electricalgenerators)
YEAR 1
1 2 3 4 5 6 7 8 9 10 11 12 1
TASKS
1
2
3
Ability to deal with unprecedented faults(faults never seen in the past)Data mining to understand relationsbetween subsystems of a highly complex(multi-physics) systemPotentials to dramatically shortenequipment downtimes and their impacts
• Method for identifying relationsbetween subsystems in a highlycomplex system of interactingdynamic subsystems
• Demonstration of faultlocalization in a complexsystem in which causalrelations between subsystemsare not a priori known(semicond. dataset)
20 BUDGET SUMMARY
• Development of the method forcausation discovery and oneapplication area, such as plasmabased semiconductormanufacturing tool
• $40K (student tuition andstipend for a year)
• Budget for a second applicationexample
• Additional $20K (student tuitionand support for 6 months)
• Total: $40-60K
Year 1 Budget Year 2 Budget
• Development of the method foroptimization of policies for distributionof anomaly detectors, with oneapplication area, such as plasma basedsemiconductor manufacturing tool orautomotive engine
• $40K (student tuition and stipendfor a year)
• Development of the method forpredicting signatures of the monitoredsystem, with one application area(semiconductor manufacturing dataset)
• 40K (student tuition and stipend fora year)
• Additional $20K for anotherapplication dataset (studentsupport and tuition for 6 months)
• Total: $80-100K (could be split into 2
years)
THANK YOU
