Anomaly Detection Using Self-Organizing Maps-Based K-Nearest Neighbor Algorithm

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Jing Tian Michael H. Azarian Michael Pecht

Abstract

Self-organizing maps have been used extensively for condition-based maintenance, where quantization errors of test data referring to the self-organizing maps of healthy training data have been used as features. Researchers have used minimum quantization error as a health indicator, which is sensitive to noise in the training data. Some other researchers have used the average of the quantization errors as a health indicator, where the best matching units of the trained self-organizing maps are required to be convex. These requirements are not always satisfied. This paper introduces a method that improves self-organizing maps for anomaly detection by addressing these issues. Noise dominated best matching units extracted from the map trained by the healthy training data are removed, and the rest are used as healthy references. For a given test data observation, the k-nearest neighbor algorithm is applied to identify neighbors of the observation that occur in the references. Then the Euclidean distance between the test data observation and the centroid of the neighbors is calculated as a health indicator. Compared with the minimum quantization error, the health indicator extracted by this method is less sensitive to noise, and compared with the average of quantization errors, it does not put limitations on the convexity or distribution of the best matching units. The result was validated using data from experiments on cooling fan bearings.

How to Cite

Tian, J., Azarian, M. H., & Pecht, M. (2014). Anomaly Detection Using Self-Organizing Maps-Based K-Nearest Neighbor Algorithm. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1554
Abstract 545 | PDF Downloads 403

##plugins.themes.bootstrap3.article.details##

Keywords

anomaly detection, bearing fault detection, Rolling element bearing, K-nearest neighbor, Self-organizing maps

References
Bianchini, C., Immovilli, F., Cocconcelli, M., Rubini, R., & Bellini, A. (2011). Fault detection of linear bearings in brushless AC linear motors by vibration analysis. IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 1684-1694.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), vol. 41, no. 3, pp. 15:1-15:58.
He, D., Li, R., & Zhu, J. (2013). Plastic bearing fault diagnosis based on a two-step data mining approach. IEEE Transactions on Industrial Electronics, vol. 60, no. 8, pp. 3429-3440.
Huang, R., Xi, L., Li, X., Richard Liu, C., Qiu, H., & Lee, J. (2007). Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing, vol. 21, no.1, pp. 193-207.
Immovilli, F., Bellini, A., Rubini, R., & Tassoni, C. (2010). Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison. IEEE Transactions on Industry Applications, vol. 46, no. 4, pp. 1350-1359.
Jin, X., Ma, E. W., Cheng, L. L., & Pecht, M. (2012). Health monitoring of cooling fans based on Mahalanobis distance with mRMR feature selection. Instrumentation and Measurement, IEEE Transactions on, vol. 61, no.8, pp. 2222-2229.
Kang, P., & Birtwhistle, D. (2003). Condition assessment of power transformer onload tap changers using wavelet analysis and self-organizing map: field evaluation. IEEE Transactions on Power Delivery, vol. 18, no.1, pp. 78-84.
Kohonen, T. (1990), The self organizing map. Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480.
Lau, E. C., & Ngan, H. W. (2010). Detection of motor bearing outer raceway defect by wavelet packet transformed motor current signature analysis. IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 10, pp. 2683-2690.
Oh, H., Azarian, M. H., & Pecht, M. (2011). Estimation of fan bearing degradation using acoustic emission analysis and Mahalanobis distance. Proceedings of the Applied Systems Health Management Conference (pp. 1-12), May 10-12, Virginia Beach, VA, U.S.A.
Oh, H., Shibutani, T., & Pecht, M. (2012). Precursor monitoring approach for reliability assessment of cooling fans. Journal of Intelligent Manufacturing, Vol. 23, no. 2, pp. 173-178.
Pecht, M. (2008). Prognostics and Health Management of Electronics. New York: Wiley-Interscience, New York.
Qiu, H., Lee, J., Lin, J., & Yu, G. (2003). Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Advanced Engineering Informatics, vol. 17, no. 3, pp. 127-140.
Sotiris, V. A., Tse, P. W., & Pecht, M. G. (2010). Anomaly detection through a Bayesian support vector machine. , IEEE Transactions on Reliability, vol. 59, no. 2, pp. 277-286.
Tian, J., Morillo, C., & Pecht, M. G. (2013). Rolling element bearing fault diagnosis using simulated annealing optimized spectral kurtosis. IEEE Conference on Prognostics and Health Management (PHM), (pp. 1-5), June 24-27, Gaithersburg, MD, U.S.A.
Tian, X. (2006), Cooling fan reliability: failure criteria, accelerated life testing, modeling and qualification. Proceedings of 2006 Reliability and Maintainability Symposium (pp. 380-384), Jan 23-26, Newport Beach, CA, U.S.A.
Wang, G., Liu, C., & Cui, Y. (2012). Clustering diagnosis of rolling element bearing fault based on integrated Autoregressive/Autoregressive Conditional Heteroscedasticity model. Journal of Sound and Vibration, vol. 331, no. 19, pp. 4379-4387.
Section
Technical Papers