Pervasive Diagnosis

In model-based production, a planner uses a system description to create plans that achieve production goals. The same description can be used by model-based diagnosis to infer the condition of components from sensor data. When production is realized by a sequence of plans, prior work has demonstrat...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2010-09, Vol.40 (5), p.932-944
Main Authors: Kuhn, Lukas, Price, Bob, Minh Do, Liu, Juan, Rong Zhou, Schmidt, Tim, de Kleer, Johan
Format: Article
Language:English
Subjects:
Active probing
Automatic control
control
Degradation
diagnosis
Digital printing
Electrical equipment industry
Mass production
model-based reasoning
planning
Production costs
Production systems
Productivity
Real time systems
scheduling
Sensor phenomena and characterization
Studies
System testing
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Summary:In model-based production, a planner uses a system description to create plans that achieve production goals. The same description can be used by model-based diagnosis to infer the condition of components from sensor data. When production is realized by a sequence of plans, prior work has demonstrated that diagnosis can be used to adapt the plans to compensate for component degradation. However, the sources of diagnostic information are severely limited. Diagnosis must either make inferences from observations during production over which it has no control (passive diagnosis), or production must be halted to introduce diagnostic-specific plans (explicit diagnosis). We observe that the declarative nature of the model-based approach allows the planner to achieve production goals in multiple ways. This flexibility is exploited by a novel paradigm, i.e., pervasive (active) diagnosis , which constructs informative production plans that simultaneously achieve production goals while uncovering additional diagnostic information about the condition of components. We present an efficient heuristic search for these informative production plans and show through experiments on a model of an industrial digital printing press that the theoretical increase in long-run productivity can be realized on practical real-time systems. We obtain higher long-run productivity than a decoupled combination of planning and diagnosis.
ISSN:1083-4427
2168-2216
1558-2426
2168-2232
DOI:10.1109/TSMCA.2010.2052040