TMS - Prognosis · 2007. 2. 13. · TMS 2003 Annual Conference San Diego, California L....
Transcript of TMS - Prognosis · 2007. 2. 13. · TMS 2003 Annual Conference San Diego, California L....
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PrognosisTMS 2003 Annual Conference
San Diego, California
L. Christodoulou
Defense Science Office
Defense Advanced Research Projects Agency
J. M. Larsen
Materials Directorate
Air Force Research Laboratory
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AcknowledgementsAcknowledgements
TMS for sponsoring the symposium
Session organizing committee
Numerous contributors who have helped shape our thinking on the subject
Sessions contributors
DEDICATED TO THE LATE ARTHUR DINESS --A GREAT FRIEND AND INSPIRATION
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Goals of the Presentation Goals of the Presentation
Define “Prognosis”
Establish the context for “Prognosis”
Encourage collaboration across disciplines (materials, sensor technology, data processing and fusion, feature extraction, etc.).
Provide an example of an approach
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DefinitionDefinition
PROGNOSIS: -- Knowledge of future performance based on reliable prediction capability of individual platforms.
Context: -- Materials and Structures
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Prognosis - Power is Knowing the FuturePrognosis - Power is Knowing the Future
Delphi Oracle
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Prognosis-based Asset Management Approach Prognosis-based Asset Management Approach
• Management, deployment and use of assets based on PROGNOSIS -- knowledge of future performance based on reliable prediction capability of individual platforms.
• Managing according to knowledge of the individual and actual remaining performance
• Managing uncertainty by reliable (physics-based?) predictive capability
• Enabling material “state awareness”
Prognosis Translates Knowledge and Information Richness to Physical Capability
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Presently, “Fear of Failure” controls our design, management, deployment and use of all critical elements of complex mechanical systems (aircraft, helicopters, space vehicles, submarines, ships, UAVs, etc.).
• Forces undue conservatism (large safety factors) reducing performance.
• Severely impacts system availability and readiness.
• Forces non-optimal use of available assets.
• Results in high cost.
Present ParadigmPresent Paradigm
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Fear is Justified: Materials Failure Matters!Fear is Justified: Materials Failure Matters!
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Fear is Justified: Materials Failure Matters!Fear is Justified: Materials Failure Matters!
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Fear is Justified: Materials Failure Matters!Fear is Justified: Materials Failure Matters!
PROBABLE CAUSE: "The National Transportation Safety Board determines that the probable cause of this accident was the inadequate consideration given to human factors limitations in the inspectionand quality control procedures used by United Airlines' engine overhaul facility which resulted in the failure to detect a fatigue crack originating from a previously undetected metallurgical defect located in a critical area of the stage 1 fan disk……... The subsequent catastrophic disintegration of the disk result in the liberation of debris in a pattern of distribution and with energy levels that exceeded the level of protection provided by design features of the hydraulic systems that operate the DC-10's flight controls." (NTSB/AAR-90/06)
Date: 19 JUL 1989Type: McDonnell Douglas DC-10-10Operator: United Air Flight 232Registration: N1819UYear built: 1973Total airframe hrs: 43401 hours Cycles: 16997 cyclesTotal: 111 fatalities / 296 on board Location: Sioux City-Gateway, IA
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The PresentThe Present
Database:Mission History,
Maintenance, Life Extension and Design.
Insp
ect
Decision
Yes
NO
Empirical Criteria
Management, deployment and use of high value systems is dominated by our fear of failure
Failure Occurrences
Usage (D
uty Cycles)
“Book” Life: 99.9 % Unfailed
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Impact of UncertaintyImpact of Uncertainty
Conde
mned
Engine
Disks
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The Prognosis VisionThe Prognosis Vision
Database:Mission History,
Maintenance, Life Extension and Design.
Yes
NO
Prognosis
Failure physics, damage evolution,predictive models
Capability Profile, e.g
speed
altit
ude
Stat
e A
war
enes
s
Interrogation
Prognosis Translates Knowledge and Information Richness to Physical Capability
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Life Management Through PrognosisLife Management Through Prognosis
log Life (e.g. Cycles or TACs)
Failu
re
8000(Design Life)
log Life (e.g. Cycles or TACs)
Failu
re
(Near Actual Life)
Improved Life Prediction
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Technical ApproachTechnical Approach
What are the enabling elements of a Prognosis paradigm?
How would one practice Prognosis?
What would one actually do?
How do you go from “dislocations” to throttle settings?
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Interrogation and State Awareness Interrogation and State Awareness
Conceptual:• Not inspection• Allows the material and structure to communicate
its statePractical:
• Local (embedded/in-situ) or global information• Multi-spectral, -spatial, temporal• May require external perturbation or pre-defined
maneuver(s)• Benchmarked (initially and subsequently?)• MAY demand inspection (last resort)
Computational:• Feature extraction• Dimensionality reduction• Reliable error estimation
Stat
e A
war
enes
sInterrogation
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InfraredCamera
Physics of FailurePhysics of Failure
Crystal Plasticity Models Scalable Computational Models and Algorithms
Auxiliary Models
Physically BasedLife Prediction Model
Model Development
Model Validation Life Prediction
Crack Initiation andGrowth Prediction
Analytical Predictions• Polycrystal Plasticity• Machining Defects• Inclusions
IDDSInteractive Analysis and
Experiment
Displacement Field Mapping
OIM
Conventional Testing
Probabilistic Material Characterization
• Explicit FEA• Adaptive Meshing• Multi-scale Models
• Subscale Processes• Subscale Properties• Defect Distributions• Microstructural Data• Residual Stresses
300
400
500
600
10 -1 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8
σm
ax (M
Pa)
N (Cycles)
3
4
5
6
7
8
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10
0 100 200 300 400 500 600 700 800
∆K t
h (M
Pa¦m
)
Temperature (°C)
10 -10
10 -9
10 -8
10 -7
10 -6
10 -5
6 7 8 9 10 20 30
da/d
N (m
/cyc
le)
∆K (MPa¦m)
1
2
3
4
5
0 1 10 6 2 10 6 3 10 6 4 10 6
RT600°C800°C
Nor
mal
ized
CM
OD
Cycles
K5-DuplexR=0.1
300
400
500
600
10 -1 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8
σm
ax (M
Pa)
N (Cycles)
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Creep HCF LCF
Fret. Embr. Envr.
Physics of Failure
?
? ?
?
The LinkThe LinkSystem Parameters (throttle setting)
Component, e.g., disk
Evolving Material Microstructure
Temperature, stress, time, environment, etc. (incl. distribution)
inputs to state equations
CFD, FEMTemperature, stress, time,
environment, etc. (incl. distribution)
Interrogation Tools for State Awareness
LocalLaser UltrasonicsThermoacousticThermoelectric
GlobalThermalAcousticVibration
Crack Detected
Failure
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Failure is Neither Random or UnpredictableFailure is Neither Random or Unpredictable
Failure mode DOMAINS well defined (fatigue, creep, corrosion, etc.)
Failure is progressive:
NUCLEATION/INITIATION
PROPAGATION/ESCALATION
COALESCENCE
Reliable failure PREDICTION will be accomplished by combination of;1. Models of physics of failure
Evolution of damageCoupled effects
2. Interrogation tools for state awarenessLocal AND globalSignature manifestations
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Conceptual Model/State Awareness FusionConceptual Model/State Awareness Fusion
Knowledge of failure domains establishes functional behavior of damage evolution.
Tracking changes not absolute values.
Fidelity/reliability increases with prognosis system usage and maturity.
Short term predictions more reliable than long term “lifing” predictions.
Uncertainty in model predictions modulated by state awareness tools.
Dam
age
Mission/Usage
Dcrit.
Time of prediction
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How Does it Work?How Does it Work?
0
20
40
60
80
100
120
140
160
180
0 5000 10000 15000 20000
Time, Sec
Stre
ss, k
si
0
100
200
300
400
500
600
Tem
pera
ture
, F
StressTemperature
Model Mission State at time, t
State at time, t+∆t
Signature of “new state” provides confidence that model prediction is
within expected regime.
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Vision: Materials Damage PrognosisVision: Materials Damage Prognosis
Key science and technology disciplinesCoupled physics-based models of materials damage and behavior
• Interaction of multiple damage/failure mechanisms• Multi-scale, mechanism-based• Microstructurally-based stochastic behavior• Integrated information from state-awareness tools
Interrogation of damage-state• Intelligently exploit existing sensors• Feature extraction from global sensors• Materials-damage-state interrogation techniques and
recorders• Sensor signature analysis
Data management and fusion• Capability matched to mission • Component usage data• Component history and pedigree
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Concluding RemarksConcluding Remarks
• Urge vigorous discussion
• Constructive dialogue not (just) criticism
• Thinking “out of the box”
• Encourage inter- and multidisciplinary research
• Attack the hard problems
• How do use sensor data to update model predictions?
• How to efficiently link localized (micro) damage to macro behavior?
• How can complexity be reduced?
• Accept that models, sensors and IT techniques will continue to evolve and improve thus attempt to establish methodologies/protocols for communicating, fusing and exploiting the different disciplines now.
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Questions?
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Interrogation and State Awareness Interrogation and State Awareness
Conceptual:• Not inspection• Allows the material and structure to communicate
its statePractical:
• Local (embedded/in-situ) or global information• Multi-spectral, -spatial, temporal• May require external perturbation or pre-defined
maneuver(s)• Benchmarked (initially and subsequently?)• MAY demand inspection (last resort)
Computational:• Feature extraction• Dimensionality reduction• Reliable error estimation
Stat
e A
war
enes
sInterrogation
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Existing Database (History and Past Missions)Existing Database (History and Past Missions)
DO REALLY use past mission history
• Identify salient features of every mission
DO take into account knowledge of the system behavior
• Track trends
DO take into account maintenance history
Exploit expert knowledge
Leverage previous efforts
Exploit IT revolution.
Database:Mission History,
Maintenance, Life Extension and
Design.
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Damage Evolution Damage Evolution
Use knowledge of applicable physics.
Invoke and exploit coupled and interacting mechanisms.
Use multiple models (if available).
Physics-based and data-driven models will evolve—allow for updates.
Reduced and full models.
Sensor data can modulate model predictions.
Failure physics, damage evolution,predictive models
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Database:Mission History,
Maintenance, Life Extension and Design.
Prognosis
Failure physics, damage evolution,predictive models
Stat
e A
war
enes
s
Interrogation
Prognosis Reasoning SystemPrognosis Reasoning System
Integrates of all elements, system knowledge and logic
Predicts of capability
Provides multiple decision makers the required information (operator, local commander, theatre director, maintenance, etc.
Provides confidence levels on predictions
Employs sophisticated and evolving reasoners
Conveys pertinent information for easy assimilation
Relies on local and rapid e.g. onboard (reduced) response and more complete e.g., remote control center (full) system models
Benchmarked at convenient times and locations
Based on open and modular architecture
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Prognosis ContentPrognosis Content
• Science and Technology• Predictive, coupled, multi-scale damage evolution/physics of failure models• (Non-intrusive) state awareness techniques and tools.• Math techniques for feature extraction and characterization of the state of the
system.• Performance projection capability based on current state.• Adaptive mission strategies and (on-board) reasoning/intelligence system• Tools to give multiple users reliable and accurate capability status in “real time”
• Technology• Existing/develop test beds to validate tools and models• Leverage data fusion technologies to implement Prognosis architecture and
reasoning system• Exploit effective data mining techniques (from IT?)
• Demonstrations• Demonstrate impact through analysis and physical demonstrations• Deliver decision tools for pervasive (sub)system manned or unmanned systems.
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