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Engine assembly is a complex and heavily automated distributed-controlprocess, with large amounts of faults data logged everyday. We describe anapplication of temporal data mining for analyzing fault logs in an engineassembly plant. Frequent episode discovery framework is a model-free methodthat can be used to deduce (temporal) correlations among events from the logsin an efficient manner. In addition to being theoretically elegant andcomputationally efficient, frequent episodes are also easy to interpret in theform actionable recommendations. Incorporation of domain-specific informationis critical to successful application of the method for analyzing fault logs inthe manufacturing domain. We show how domain-specific knowledge can beincorporated using heuristic rules that act as pre-filters and post-filters tofrequent episode discovery. The system described here is currently being usedin one of the engine assembly plants of General Motors and is planned foradaptation in other plants. To the best of our knowledge, this paper presentsthe first real, large-scale application of temporal data mining in themanufacturing domain. We believe that the ideas presented in this paper canhelp practitioners engineer tools for analysis in other similar or relatedapplication domains as well.

Temporal data mining for root-cause analysis of machine faults in automotive assembly lines