Temporal data mining for root-cause analysis of machine faults in automotive assembly lines
作者： Srivatsan Laxman , Basel Shadid , K. P. Unnikrishnan , P. S. Sastry
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摘要: Engine assembly is a complex and heavily automated distributed-control process, with large amounts of faults data logged everyday. We describe an application temporal mining for analyzing fault logs in engine plant. Frequent episode discovery framework model-free method that can be used to deduce (temporal) correlations among events from the efficient manner. In addition being theoretically elegant computationally efficient, frequent episodes are also easy interpret form actionable recommendations. Incorporation domain-specific information critical successful manufacturing domain. show how knowledge incorporated using heuristic rules act as pre-filters post-filters discovery. The system described here currently one plants General Motors planned adaptation other plants. To best our knowledge, this paper presents first real, large-scale believe ideas presented help practitioners engineer tools analysis similar or related domains well.
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128.84.21.199参考文章(12)
Harpreet S. Sawhney, King-Ip Lin, Kyuseok Shim, Rakesh Agrawal, Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases very large data bases. pp. 490- 501 ,(1995)
Georg Dorffner, Peter Tino, Christian Schittenkopf, Temporal pattern recognition in noisy non-stationary time series based on quantization into symbolic streams. Lessons learned from financial volatility trading. SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business. ,(2000)
John F. Roddick, Myra Spiliopoulou, A bibliography of temporal, spatial and spatio-temporal data mining research ACM SIGKDD Explorations Newsletter. ,vol. 1, pp. 34- 38 ,(1999) , 10.1145/846170.846173
Fabian Mörchen, Unsupervised pattern mining from symbolic temporal data ACM SIGKDD Explorations Newsletter. ,vol. 9, pp. 41- 55 ,(2007) , 10.1145/1294301.1294302
Srivatsan Laxman, P. Sastry, K. Unnikrishnan, Discovering Frequent Generalized Episodes When Events Persist for Different Durations IEEE Transactions on Knowledge and Data Engineering. ,vol. 19, pp. 1188- 1201 ,(2007) , 10.1109/TKDE.2007.1055
Srivatsan Laxman, P. S. Sastry, K. P. Unnikrishnan, A fast algorithm for finding frequent episodes in event streams Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07. pp. 410- 419 ,(2007) , 10.1145/1281192.1281238
Heikki Mannila, Hannu Toivonen, A. Inkeri Verkamo, Discovery of Frequent Episodes in Event Sequences Data Mining and Knowledge Discovery. ,vol. 1, pp. 259- 289 ,(1997) , 10.1023/A:1009748302351
R. Agrawal, R. Srikant, Mining sequential patterns international conference on data engineering. pp. 3- 14 ,(1995) , 10.1109/ICDE.1995.380415
Srivatsan Laxman, P.S. Sastry, K.P. Unnikrishnan, Discovering frequent episodes and learning hidden Markov models: a formal connection IEEE Transactions on Knowledge and Data Engineering. ,vol. 17, pp. 1505- 1517 ,(2005) , 10.1109/TKDE.2005.181
Srivatsan Laxman, P. S. Sastry, A survey of temporal data mining Sadhana-academy Proceedings in Engineering Sciences. ,vol. 31, pp. 173- 198 ,(2006) , 10.1007/BF02719780