The generation of diagnostic heuristics for automated error recovery in manufacturing workstations

Abstract
The authors describe an approach using machine learning for developing heuristics to be used in diagnosis for automated error recovery in manufacturing systems. This approach can automatically generate diagnostic heuristics for error hypotheses. The approach is based on the integration of set covering and explanation-based learning. In this way, the diagnostic heuristics can be automatically generated and the competing errors can be narrowed down, if not identified. In addition, the set covering concept is potentially useful for diagnostic problems, since it provides a solution to the problem of multiple simultaneous errors. The set covering theory and the theoretical development based on the approach for diagnosing the error and for automatically generating diagnostic heuristics are given. The authors also propose an approach for future research to extract discriminating knowledge for the competing errors when set covering is incapable of identifying the error. A discussion of results thus far obtained, accompanied by simple examples to illustrate those results, is presented.

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