Paper Info
Title
Temporal data mining for root-cause analysis of machine faults in automotive assembly lines
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.
Year
Venue
Keywords
2009
Clinical Orthopaedics and Related Research
root cause analysis
Field
DocType
Volume
Data mining,Heuristic,General motors,Root cause analysis,Artificial intelligence,Temporal data mining,Mathematics,Machine learning,Automotive industry
Journal
abs/0904.4
Citations 
PageRank 
References 
1
0.39
7
Authors
4
Name
Order
Citations
PageRank
Srivatsan Laxman142121.65
Basel Shadid210.39
P. S. Sastry374157.27
K. P. Unnikrishnan429923.21