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