Case Study: Models for Detecting Low Oil Pressure Anomalies on Commercial Vehicles

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Howard E. Bussey Nenad G. Nenadic Paul A. Ardis Michael G. Thurston

Abstract

We present a case study of anomaly detection using commercial vehicle data (from a single vehicle collected over a six-month interval) and propose a failure-event analysis. Our analysis allows performance comparison of anomaly detection models in the absence of sufficient anomalies to compute the Receiver Operating Characteristic curve.

Several heuristically-guided data-driven models were considered to capture the relationship among three main engine signals (oil pressure, temperature, and speed). These models include regression-based approaches and distance-based approaches; the former use the residual’s z-score as the detection metric, while the latter use a Mahalanobis distance or similar measure as the metric. The selected regression-based models (Boosted Regression Trees, Feed-Forward Neural Networks, and Gridded Regression tables) outperformed the selected distance-based approaches (Gaussian Mixtures and Replicator Neural Networks). Both groups of models were superior to existing Diagnostic Trouble Codes. The Gridded Regression tables and Boosted Regression Trees exhibited the best overall metric performance.

We report a surprising behavior of one of the models: locally- optimal Gaussian Mixture Models often had zero detection performance, with such models occurring in at least 25% of the iterations with seven or more Gaussians in the mixture. To overcome the problem, we propose a regularization method that employs a heuristic filter for rejecting Gaussian Mixtures with non-discriminative components.

How to Cite

E. Bussey, . H. ., G. Nenadic, N. ., A. Ardis, P. ., & G. Thurston, . M. . (2014). Case Study: Models for Detecting Low Oil Pressure Anomalies on Commercial Vehicles. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2416
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References
Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.

Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 15.

Cheifetz, N., Same, A., Aknin, P., & de Verdalle, E. (2011). A pattern recognition approach for anomaly detection on buses brake system. In Intelligent transportation systems (itsc), 2011 14th international ieee conference on (pp. 266–271).

Duda, R., Hart, P., & Stork, D. (2000). Pattern classification. 2nd edn wiley.

Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4).

Figueiredo, M., & Jain, A. (2002, march). Unsupervised learning of finite mixture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(3), 381 -396. doi: 10.1109/34.990138

Golosinski, T. S., Hu, H., & Elias, R. (2001). Data mining vims data for information on truck condition. Computer Applications in the Minerals Industries, 88, 397– 402.

Hawkins, S., He, H., Williams, G., & Baxter, R. (2002). Outlier detection using replicator neural networks. In Y. Kambayashi, W. Winiwarter, & M. Arikawa (Eds.), Data warehousing and knowledge discovery (Vol. 2454, p. 113-123). Heidelberg: Springer.

Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair, P., Bushra, S., ... others (2004). Vedas: A mobile and distributed data stream mining system for real-time ve- hicle monitoring. In Proceedings of siam international conference on data mining (Vol. 334).

McArthur, S. D. J., Booth, C. D., McDonald, J. R., & Mc- Fadyen, I. T. (2005). An agent-based anomaly detection architecture for condition monitoring. Power Systems, IEEE Transactions on, 20(4), 1675-1682.

Nowlan, F., & Heap, H. (1978). Reliability-centered maintenance (Tech. Rep. No. AD/A066 579). United Airlines, San Francisco, CA.
Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Process- ing, 25(5), 1803-1836.

Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Hoboken, NJ: John Wiley & Sons, Inc.
Vnomics. (2012). Products - vehicle health management software (Vol. 2012) (No. 3/16/2012).
Section
Technical Papers

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