Abstract
Air leakage in braking pipes is a commonly encountered mechanical defect on trains. A severe air leakage will lead to braking issues and therefore decrease the reliability and cause train delays or stranding. However, air leakage is difficult to be detected via visual inspection and therefore most air leakage defects are run to fail. In this study we present a contextual anomaly detection method that detects air leakage based on the on/off logs of a compressor. Air leakage causes failure in the context when the compressor idle time is short than the compressor run time, that is, the speed of air consumption is faster than air generation. In our method the logistic regression classifier is adopted to model two different classes of compressor behavior for each train separately. The logistic regression classifier defines the boundary separating the two classes under normal situations and models the distribution of the compressor idle time and run time separately using logistic functions. The air leakage anomaly is further detected in the context that when a compressor idle time is erroneously classified as a compressor run time. To distinguish anomalies from outliers and detect anomalies based on the severity degree, a density-based clustering method with a dynamic density threshold is developed for anomaly detection. The results have demonstrated that most air leakages can be detected one to four weeks before the braking failure and therefore can be prevented in time. Most importantly, the contextual anomaly detection method can pre-filter anomaly candidates and therefore avoid generating false alarms.
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Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and Naive Bayes. In: Advances in Neural Information Processing Systems, vol. 14, pp. 841–848 (2001)
Hayes, M.A., Capretz, M.A.: Contextual anomaly detection framework for big sensor data. J. Big Data 2, 2 (2015)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)
Khan, L., Awad, M., Thuraisingham, B.: A new intrusion detection system using support vector machines and hierarchical clustering. VLDB J. 16(4), 507–521 (2007)
Upadhyaya, S., Singh, K.: Nearest neighbour based outlier detection techniques. Int. J. Comput. Trends Technol. 3(2), 299–303 (2012)
Mahapatra, A., Srivastava, N., Srivastava, J.: Contextual anomaly detection in text data. Algorithms 4, 469–489 (2012)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.W.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Behera, S., Rani, R.: Comparative analysis of density based outlier detection techniques on breast cancer data using hadoop and map reduce. In: Proceedings of the International Conference on Inventive Computation Technologies (2016)
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Lee, WJ. (2017). Contextual Air Leakage Detection in Train Braking Pipes. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_22
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DOI: https://doi.org/10.1007/978-3-319-60045-1_22
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