### abstract ###
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
### introduction ###
Automotive engine assembly is a heavily automated and complex process that is  controlled in a distributed fashion
Each assembly plant consists of several machines (or operations/stations) that are extensively inter-connected and are programmed to automatically execute the various operations necessary to manufacture an automotive engine
The distributed control system maintains elaborate logs regarding the time-evolving conditions of all machines in the plant, the status of different operations performed on a particular engine, the throughput statistics of the plant, etc
In this paper, we present an application that uses temporal data mining techniques for analyzing these time-stamped logs to help in  fault analysis and root-cause diagnosis
The application presented here is currently being used on a regular basis in engine assembly plants of General Motors }
