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

TL;DR

This paper presents a large-scale application of temporal data mining using frequent episode discovery to analyze fault logs in automotive engine assembly lines, aiding root-cause analysis and operational improvements.

Contribution

It introduces a novel, efficient, and interpretable method for fault log analysis that incorporates domain-specific knowledge, marking the first large-scale application in manufacturing.

Findings

Effective identification of fault correlations

Incorporation of domain knowledge improves analysis

System successfully deployed at GM plant

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.