Augmenting Experience-Based Diagnosis With Causal Reasoning

David, Jean-Marc; Krivine, Jean-Paul

doi:10.1080/08839518908949926

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…An expert system for the fault diagnosis of rotating machinery is reported in [19]. The knowledge base consists of over 1500 heuristic rules, and includes a causal fault knowledge explanation facility that provides the nonexpert user with an explanation of the diagnosis to increase confidence in the system.…”

Section: B Condition Monitoring Of Turbine Generators Using Expert Smentioning

confidence: 99%

A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge

Todd

McArthur

McDonald

et al. 2007

IEEE Trans. Syst., Man, Cybern. C

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Condition monitoring of turbine generators, housed at British Energy nuclear power stations throughout the U.K., is implemented to diagnose incipient faults at an early stage, so corrective action can be taken to avoid the associated high costs of an unplanned shutdown. A prototype expert system has been developed that provides decision support to condition monitoring experts who monitor British Energy turbine generators. The expert system automatically interprets data from strategically positioned sensors and transducers on the turbine generator by applying expert knowledge in the form of heuristic rules. This paper reviews the application domain and describes the work undertaken in developing the prototype expert system. The paper also outlines a learning module design that uses an approach based on an analytical symbolic machine learning technique, explanation-based generalization, to semiautomatically derive heuristic rules for turbine generator fault diagnosis. The approach adopted by the learning module is explained in detail and a worked example demonstrates how the learning module can derive a fault heuristic from a single training example. The modular approach to capturing the causal fault and behavioral models is described, and the method in which the module will be integrated with the existing expert system has been outlined. A preliminary evaluation of the learning module design is discussed.Index Terms-Expert systems, fault diagnosis, knowledge acquisition, knowledge-based systems, learning systems, power system monitoring.

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Section: B Condition Monitoring Of Turbine Generators Using Expert Smentioning

confidence: 99%

A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge

Todd

McArthur

McDonald

et al. 2007

IEEE Trans. Syst., Man, Cybern. C

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“…Recently, a new development has emerged in expert system explanation. In this new research, explanation is no longer viewed as an add-on to the expert system's reasoning, but as a problemsolving activity in its own right (David & Krivine, 1989;Moore & Swartout, 1989;Ryan & Bridges, 1988;Tanner & Josephson, 1988). This new research appears to be headed towards adding a fourth core idea to expert system explanation.…”

Section: Introductionmentioning

confidence: 99%

Explanation as a Primary Task in Problem Solving

Wick

1994

The Knowledge Engineering Review

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This article summarizes the author's perspective on the discussions that occurred at the Workshop on Explanation and Problem Solving held during the Thirteenth International Joint Conference on Artificial Intelligence*. Motivated by those discussions, the article argues for the promotion of expert system explanation from a secondary task, used mainly for communication, to a primary task that is tightly integrated with the domain problem solving of the expert system.

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