1. Model-based Diagnosis: The Single Fault Case
Brian C. Williams J/6.834J October 9th, 2002
9/13/00
copyright Brian Williams, 2000
courtesy of JPL
Brian C. Williams, copyright 2000

2. act WORLD Plant actions observations P(s) Diagnostic Agent:
3/6/00
WORLD
Plant
actions
observations
P(s)
sense
act
Diagnostic Agent:
Monitors & Diagnoses
Repairs & Avoids
Probes and Tests
AGENT
Symptom-based
Consistency-based
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copyright Brian Williams, 2000

3. Outline Single Fault Diagnosis Explaining the symptoms
Handling the unknown
9/13/00
copyright Brian Williams, 2000

4. Failures may be hidden: STS-93
Symptoms:
Engine temp sensor high
LOX level low
GN&C detects low thrust
H2 level low (???)
Problem: Liquid hydrogen leak
Effect:
LH2 used to cool engine
Engine runs hot
Consumes more LOX
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copyright Brian Williams, 2000

5. What is Fault Diagnosis?3 M1 X F 10 B 2 A1 C 2 M2 Y D G 12 3 A2 E M3 Z 3
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copyright Brian Williams, 2000

6. What is Fault Diagnosis?
Model-based Diagnosis: Given a system with symptomatic behavior and a model of the system, find diagnoses that account for symptoms. A 6
Symptom 12 3 M1 X F 10 B 2 A1 6 C 2 M2 Y D G 12 3 A2 E M3 Z 3
9/13/00
copyright Brian Williams, 2000

7. What is Fault Diagnosis?
Model-based Diagnosis: Given a system with symptomatic behavior and a model of the system, find diagnoses that account for symptoms. A 6
Symptom 12 3 M1 X F 10 B 2 A1 6 C 2 M2 Y D G 12 3 A2 E M3 Z 3
9/13/00
copyright Brian Williams, 2000

8. Standard Diagnostic Approach
Generate candidates from symptoms
Test if candidates account for symptoms.
Diagnoses should be complete.
Diagnoses should exploit all available information.
9/13/00
copyright Brian Williams, 2000

9. Outline Single Fault Diagnosis Explaining the symptoms
Handling the unknown
9/13/00
copyright Brian Williams, 2000

10. How Should Diagnoses Account for Symptoms?
Abductive Diagnosis: Given symptoms, find diagnoses that predict symptoms. A 6
Symptom 12 3 M1 X F 10 B 2 A1 6 C 2 M2 Y D G 12 3 A2 E M3 Z 3
M1: Drops low bit on A
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copyright Brian Williams, 2000

11. Single Fault, Abductive Diagnosis by Generate and Test
3/6/00
Single Fault, Abductive Diagnosis by Generate and Test
Given: Exhaustive fault models for each component.
Generate: Consider each single fault mode as a candidate.
Test:
1. Simulate candidate, given inputs.
2. Compare to observations.
Disagree: Discard
Agree: Keep
No prediction: Discard
If all fault models discarded, the component okay (exonerate)
Problem:
Fault models may be incomplete
May incorrectly exonerate faulty components
9/13/00
copyright Brian Williams, 2000

12. Outline Single Fault Diagnosis Explaining the symptoms
Handling the unknown
9/13/00
copyright Brian Williams, 2000

13. Failures are Often Novel
Mars Observer
Mars Climate Orbiter
Mars Polar Lander
Deep Space 2
courtesy of JPL
9/13/00
copyright Brian Williams, 2000

14. 9/13/00
copyright Brian Williams, 2000

15. How Should Diagnoses Account for Novel Symptoms?
Consistency-based Diagnosis: Given symptoms, find diagnoses that are consistent with symptoms.
Constraint Suspension: Make no presumptions about faulty component behavior. 6 12
Symptom M1 M2 M3 A1 A2 A B C D E 3 2 F G X Y Z 10
9/13/00
copyright Brian Williams, 2000

16. How Should Diagnoses Account for Novel Symptoms?
Consistency-based Diagnosis: Given symptoms, find diagnoses that are consistent with symptoms.
Constraint Suspension: Make no presumptions about faulty component behavior. A 6
Symptom 12 3 M1 X F 10 B 2 A1 6 C 2 M2 Y D G 12 3 A2 E M3 Z 3
9/13/00
copyright Brian Williams, 2000

17. How Should Diagnoses Account for Novel Symptoms?
Consistency-based Diagnosis: Given symptoms, find diagnoses that are consistent with symptoms.
Constraint Suspension: Make no presumptions about faulty component behavior. A 6 3 M1 X F 10 B 2 A1 6 C 2 M2 Y D G 12 3 A2 E M3 Z 3
9/13/00
copyright Brian Williams, 2000

18. Single Fault Diagnosis as Constraint Suspension
Find symptoms
Simulate forward (limited inference)
Generate candidates from symptom.
Identify components in conflict
Test candidates against all observations.
Suspend model of candidate
9/13/00
copyright Brian Williams, 2000

19. 1. How Do We Find Symptoms? Simplest: Forward simulation from inputs.6 A 12 3 M1 X F 10 B 2 6 A1 C 2 M2 Y D G 3 A2 12 E M3 Z 12 6
9/13/00
copyright Brian Williams, 2000

20. 1. How Do We Find Symptoms? Simplest: Forward simulation from inputs.
Compare predictions to outputs. 6 A 12 3 M1 X F 10 B 2 6 A1 C 2 M2 Y D G 3 A2 12 E M3 Z 12 6
9/13/00
copyright Brian Williams, 2000

21. 2. Generate Candidates: Identify Conflicts6 A 12 3 M1 X F F 10 B 2 6 A1 C 2 M2 Y D G 3 E Z
Symptom: F is observed 10, but should be 12 if M1& M2 & A1 are okay.
Conflict: ok(A1) & ok(M1) & ok(M2) is inconsistent
9/13/00
copyright Brian Williams, 2000

22. 2. Generate Candidates: Identify Conflicts6 A 12 3 M1 X F 10 B 2 6 A1 C 2 M2 Y D G 3 E Z
Symptom: F is observed 10, but should be 12
Conflict: ok(A1) & ok(M1) & ok(M2) is inconsistent
Candidates: not ok(A1) or not ok(M1) or not ok(M2).
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copyright Brian Williams, 2000

23. 3. Test Candidates: Constraint SuspensionM1 X 12 F 10 B 2 A1 C 2 M2 Y D G 3 A2 12 12 E M3 Z 3
Select candidate M1
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copyright Brian Williams, 2000

24. 3. Test Candidates: Constraint SuspensionX F 10 B 2 A1 C 2 M2 Y D G 3 A2 12 E M3 Z 3
Select candidate M1
Suspend M1’s constraints
9/13/00
copyright Brian Williams, 2000

25. 3. Test Candidates: Constraint SuspensionX F 10 B 2 A1 6 C 2 M2 Y D G 3 A2 12 6 12 E M3 Z 3
Select candidate M1
Suspend M1’s constraints
Predict forward
9/13/00
copyright Brian Williams, 2000

26. 3. Test Candidates: Constraint SuspensionM1 X F 10 B 2 C 2 M2 Y D G 3 A2 12 E M3 Z 3
Select candidate A1
Suspend A1’s constraints
9/13/00
copyright Brian Williams, 2000

27. 3. Test Candidates: Constraint Suspension6 3 M1 X F 10 B 2 6 C 2 M2 Y D G 3 A2 12 6 12 E M3 Z 3
Select candidate A1
Suspend A1’s constraints
Predict forward
9/13/00
copyright Brian Williams, 2000

28. 3. Test Candidates: Constraint SuspensionM1 X F 10 B 2 A1 C 2 Y D G 3 A2 12 E M3 Z 3
Select candidate M2
Suspend M2’s constraints
9/13/00
copyright Brian Williams, 2000

29. 3. Test Candidates: Constraint Suspension6 3 M1 X F 10 B 2 A1 C 2 Y D G 3 A2 12 6 E M3 Z 3
Select candidate M2
Suspend M2’s constraints
Predict forward
9/13/00
copyright Brian Williams, 2000

30. But is not ok(M2) consistent?A 6 3 M1 X 12 F 10 B 2 A1 6 C 2 Y D G 3 A2 12 6 E M3 Z 3
Historically: use Sussman/Steele Constraint Propagation:
Propagate newly assigned values through constraints mentioning variable.
To propagate, use assigned values for constraint to deduce unknown value(s) of constraint.
Incomplete inference procedure
Use complete SAT or CSP algorithm
9/13/00
copyright Brian Williams, 2000

31. Single Fault Diagnosis as Constraint Suspension
Find symptoms
Simulate forward
Generate candidates from symptom.
Identify components in conflict
Test candidates against all observations.
Suspend model of candidate
Test consistency as a CSP or SAT problem.
If consistent then it’s a diagnosis
9/13/00
copyright Brian Williams, 2000

32. 3. Test Candidates: Constraint Suspension6 3 M1 X 12 F 10 B 2 A1 6 C 2 Y D G 3 A2 12 6 E M3 Z 3
Summary: M1 and A1 are diagnoses, not M2.
9/13/00
copyright Brian Williams, 2000

33. Review: Model-based Diagnosis
A failure is a discrepancy between the model and observations of an artifact.
Diagnosis is symptom directed
Symptoms identify conflicting components as initial candidates.
Test novel failures by suspending constraints and testing consistency.
9/13/00
copyright Brian Williams, 2000