Event-driven Data Mining Techniques for Automotive Fault Diagnosis
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Abstract
The increasing sophistication of electronics in vehicular systems is providing the necessary information to perform data-driven diagnostics. Specifically, the advances in automobiles enable periodic acquisition of data from telematics services and the associated dealer diagnostic data from vehicles; this requires a data-driven framework that can detect component degradations and isolate the root causes of failures. The event-driven data consists of diagnostic trouble codes (DTCs) and the concomitant parameter identifiers (PIDs) collected from various sensors, customer complaints (CCs), and labor codes (LCs) associated with the repair. In this paper, we discuss a systematic data-driven diagnostic framework featuring data pre-processing, data visualization, clustering, classification, and fusion techniques and apply it to field failure datasets. The results demonstrated that the support vector machine (SVM) classifier with DTCs and customer complaints as features provides the best accuracy (74.3%) compared to any other classifier and that a tree- structured classifier with SVM as the base classifier at each node achieves approximately 75.2% diagnostic accuracy.
How to Cite
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diagnostic algorithm, fault diagnosis
(Burges, 1998) C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, pp. 121- 167, 1998.
(Cover et. al., 1991) Cover, T. M., and J. A. Thomas, Elements of Information Theory, Wiley, 1991.
(Duda et al., 2001) R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification: 2nd edition, John Wiley and Sons, 2001.
(Jackson, 1991) J. E. Jackson, A User’s Guide to Principal Components, John Wiley & Sons, New York, 1991.
(Kohonen, 1995) T. Kohonen, Self-organizing maps, Springer, Berlin, 1995.
(Metzler et al., 2004) D. Metzler, V. Lavrenko and W. B. Croft, “Formal Multiple Bernoulli Models for Language Modeling,” In. proc. 27th annual international ACM conference on Research and development in information retrieval, SIGIR’ 04, Sheffield, UK, 2004, pp. 540-541.
(Nomikos, 1996) P. Nomikos, “Detection and Diagnosis of Abnormal Batch Operations Based on Multi-way Principal Component Analysis,” ISA Tran., vol. 35, no. 3, pp. 259-266, 1996.
(Raghavan et al., 1999) V.Raghavan, M. Shakeri, and K. Pattipati, “Test Sequencing Algorithms with Unreliable Tests,” IEEE Transactions on Systems, Man and Cybernetics: Part A - Systems and Humans, vol. 29, no. 4, July 1999, pp. 347-357.
(Sankavaram et al., 2009) C. Sankavaram, B. Pattipati, A. Kodali, K. Pattipati, M. Azam, and S. Kumar, "Model-based and Data-driven Prognosis of Automotive and Electronic Systems", 5th Annual IEEE Conference on Automation Science and Engineering, Bangalore, India, August 22-25, 2009.
(Tu et al., 2003) F. Tu and K.R. Pattipati, “Rollout Strategies for Sequential Fault Diagnosis,” IEEE Transactions on Systems, Man, and Cybernetics: Part A: Systems and Humans, Vol. 33, No. 1, January 2003, pp. 86-99.
(Wold, et al., 1987) Wold, S., P. Geladi, K. Esbensen, and J, Ohman, “Principal Component Analysis”, Chemometrics and Intelligent Laboratory Systems, vol. 2:37-52, 1987.
(Geladi, et al., 1986) Geladi, P., and B. R. Kowalski, “Partial Least-Squares Regression: A Tutorial”, Analytica, Chemica, Acta, 1986.
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