Anomaly Detection for Noisy Data with the Mahalanobis–Taguchi System

Masato Ohkubo, Yasushi Nagata

Abstract

Purpose: Condition-based maintenance requires an accurate detection of unknown yet-to-have-occurred anomalies and the establishment of anomaly detection procedure for sensor data is urgently needed. Sensor data are noisy, and a conventional analysis cannot always be conducted appropriately. An anomaly detection procedure for noisy data was therefore developed.

Methodology/Approach: In a conventional Mahalanobis–Taguchi method, appropriate anomaly detection is difficult with noisy data. Herein, the following is applied: 1) estimation of a statistical model considering noise, 2) its application to anomaly detection, and 3) development of a corresponding analysis framework.

Findings: Engineers can conduct anomaly detection through the measurement and accumulation, analysis, and feedback of data. Especially, the two-step estimation of the statistical model in the analysis stage helps because it bridges technical knowledge and advanced anomaly detection.

Research Limitation/implication: A novel data-utilisation design regarding the acquired quality is provided. Sensor-collected big data are generally noisy. By contrast, data targeted through conventional statistical quality control are small but the noise is controlled. Thus various findings for quality acquisition can be obtained. A framework for data analysis using big and small data is provided.

Originality/Value of paper: The proposed statistical anomaly detection procedure for noisy data will improve of the feasibility of new services such as condition-based maintenance of equipment using sensor data.

References

Bishop, C.M., 2006. Pattern Recognition and Machine Learning. New York: Springer.

Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000. LOF: Identifying density-based local outliers. ACM SIGMOND Record, [e-journal] 29(2), pp.93-104. DOI: 10.1145/342009.335388.

Foidl, H. and Felderer, M. 2015. Research challenges of industry 4.0 for quality management. In: M. Felderer, F. Piazolo, W. Ortner, L. Brehm, HJ. Hof, eds., Proceedings of the International Conference on Enterprise Resource Planning Systems. Munich, Germany. 16-17 November. pp.121-137. DOI: 10.1007/978-3-319-32799-0_10.

Hotelling, H., 1947. Multivariate quality control - illustrated by the air testing of sample bombsights. In: C. Eisenhart, M.W. Hastay and W.A. Wallis, eds. 1947. Techniques of Statistical Analysis. New York: McGraw-Hill. pp.113-184.

Ide, T., Lozano, A.C., Abe, N. and Liu, Y., 2009. Proximity-based anomaly detection using sparse structure learning. In: Ch. Apte, H. Park, K. Wang and M.J. Zaki, Proceedings of the 2009 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining. Sparks, Nevada. 30 April – 02 May. pp.97-108. DOI: 10.1137/1.9781611972795.9.

Inoh, J., Nagata, Y., Horita, K. and Mori, A., 2012. Prediction accuracies of improved Taguchi's T methods compared to those of multiple regression analysis. Journal of The Japanese Society for Quality Control, [e-journal] 42(2), pp.265-277. DOI: 10.20684/quality.42.2_265.

Jackson, J.E. and Mudholkar, G.S., 1979. Control procedures for residuals associated with principal component analysis. Technometrics, [e-journal] 21(3), pp.341-349. DOI: 10.1080/00401706.1979.10489779.

Jin, X. and Chow, T.W., 2013. Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis–Taguchi system. Expert Systems with Applications, [e-journal] 40(15), pp.5787-5795. DOI: 10.1016/j.eswa.2013.04.024.

Lauritzen, S.L., 1996. Graphical Models. Oxford: Oxford University Press.
Ohkubo, M. and Nagata, Y., 2017. The Mahalanobis–Taguchi method based on graphical modeling. Japanese Journal of Applied Statistics, [e-journal] 46(1), pp.13-26. DOI: 10.5023/jappstat.46.13.

Ohkubo, M. and Nagata, Y., 2018. Anomaly detection in high-dimensional data with the Mahalanobis–Taguchi system. Total Quality Management & Business Excellence, [e-journal] 29(9-10), pp.1213-1227. DOI: 10.1080/14783363.2018.1487615.

Park, S.H., Shin, W.S., Park, Y.H. and Lee, Y., 2017. Building a new culture for quality management in the era of the Fourth Industrial Revolution. Total Quality Management & Business Excellence, [e-journal] 28(9-10), pp.934-945. DOI: 10.1080/14783363.2017.1310703.

Peng, C.F., Ho, L.H., Tsai, S.B., Hsiao, Y.C., Zhai, Y., Chen, Q., Chang, L.C. and Shang, Z., 2017. Applying the Mahalanobis–Taguchi system to improve tablet PC production processes. Sustainability, [e-journal] 9(9), 1557. DOI: 10.3390/su9091557.

Porter, M.E. and Heppelmann, J.E., 2014. How smart, connected products are transforming competition. Harvard Business Review, 92(11), pp.64-88.

Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J. and Williamson, R.C., 2001. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), pp.1443-1471.

Schwarz, G., 1978. Estimating the dimension of a model. The Annals of Statistics, [e-journal] 6(2), pp.461-464. DOI: 10.1162/089976601750264965.

Shin, W.S., Dahlgaard, J.J., Dahlgaard-Park, S.M. and Kim, M.G., 2018. A quality scorecard for the era of industry 4.0. Total Quality Management & Business Excellence, [e-journal] 29(9-10), pp.959-976. DOI: 10.1080/14783363.2018.1486536.

Taguchi, G., Chowdhury, S., and Wu, Y., 2005. Taguchi's Quality Engineering Handbook. Hoboken, NJ: Wiley.

Taguchi, G. and Jugulum, R., 2002. The Mahalanobis–Taguchi Strategy: A Pattern Technology System. New York: John Wiley & Sons.

Takahama, M. and Mikami, N., 2012. Detection of Abnormal Signs for Gas Turbine Power Plant. Journal of Quality Engineering Society, [e-journal] 20(4), pp.437-443. DOI: 10.18890/qes.20.4_45.

Woodall, W.H., Koudelik, R., Tsui, K.L., Kim, S.B., Stoumbos, Z.G. and Carvounis, C.P., 2003. A review and analysis of the Mahalanobis—Taguchi system. Technometrics, [e-journal] 45(1), pp.1-15. DOI: 10.1198/004017002188618626.

Yamanishi, K. and Takeuchi, J.I., 2002. A unifying framework for detecting outliers and change points from non-stationary time series data. In: O.R. Zaïane and R. Goebel, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton Alberta, Canada, July, 2002. New York, NY: Association for Computing Machinery. pp.676-681. DOI: 10.1145/775047.775148.

Authors

Masato Ohkubo
mst.okb0622@gmail.com (Primary Contact)
Yasushi Nagata
Ohkubo, M., & Nagata, Y. (2020). Anomaly Detection for Noisy Data with the Mahalanobis–Taguchi System. Quality Innovation Prosperity, 24(2), 75–92. https://doi.org/10.12776/qip.v24i2.1441
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