Abstract
Failure detection in machine condition monitoring involves a classification mainly on the basis of data from normal operation, which is essentially a problem of one-class classification. Inspired by the successful application of KFA (Kernel Function Approximation) in classification problems, an approach of KFA-based normal condition domain description is proposed for outlier detection. By selecting the feature samples of normal condition, the boundary of normal condition can be determined. The outside of this normal domain is considered as the field of outlier. Experiment results indicated that this method can be effectively and successfully applied to gear crack diagnosis.
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References
Chen, P., Toyota, T., He, Z.: Automated Function Generation of Symptom Parameters and Application to Fault Diagnosis of Machinery Under Variable Operating Conditions. IEEE Transaction on Systems, Man, and Cybernetics 31, 775–781 (2001)
Ypma, A.: Learning Methods for Machine Vibration Analysis and Health Monitoring, Ph.D. Dissertation. Delft University of Technology (2001)
Li, W., Shi, T., Liao, G., Yang, S.: Feature extraction and Classification of gear faults using principal component analysis. Journal of Quality in Maintenance Engineering 9, 132–143 (2003)
Addison, J., Wermter, S., McGarry, K., Macintyre, J.: Methods for integrating memory into neural networks in condition monitoring. In: Proc. International Conference on Artificial Intelligence and Soft Computing, Alberta, Canada, pp. 380–384 (2002)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, New York (2001)
Muller, K.R., Mika, S., Rätsch, G., et al.: An Introduction to Kernel-Based Learning Algorithms. IEEE Trans. on Neural Networks 12, 181–201 (2001)
Tax, D., Duin, R.: Support vector domain description. Pattern Recognition Letters 20(11-13), 1191–1199 (1999)
Schölkopf, B., Smola, A.J., Müller, K.R.: Nonlinear Component Analysis as A Kernel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)
Baudat, G., Anouar, F.: Generalized Discriminant Analysis Using a Kernel Approach. Neural Computation 12, 2385–2404 (2000)
Campbell, C., Bennett, K.P.: A linear programming approach to novelty detection. Advances in NIPS, vol. 14. MIT Press, Cambridge (2001)
Tax, D., Ypma, A., Duin, R.: Support vector data description applied to machine vibration analysis. In: Boasson, M., Kaandorp, J., Tonino, J., Vosselman, M. (eds.) Proc. 5th Annual Conference of the Advanced School for Computing and Imaging, Heijen, pp. 398–405 (1999)
Tax, D., Ypma, A., Duin, R.: Pump Failure Detection Using Support Vector Data Descriptions. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds.) IDA 1999. LNCS, vol. 1642, pp. 415–426. Springer, Heidelberg (1999)
Li, W.H., Shi, T.L., Yang, S.Z.: Mechanical Fault Classification Using Nonlinear Discriminant Analysis. Journal of Vibration Engineering 18, 133–138 (2005)
Li, W.H., Liao, G.L., Shi, T.L.: Kernel Principal Component Analysis and its Application in Gear Fault Diagnosis. Chinese Journal of Mechanical Engineering 39, 65–70 (2003)
Baudat, G., Anouar, F.: Kernel-based Methods and Function Approximation. In: Proceedings of International Joint Conference on Neural Networks, Washington D.C, pp. 1244–1249 (2001)
Zhang, G.: Gear Vibration Signature Analysis and Fault Diagnosis, Master thesis. Xi’an Jiaotong University. Xi’an (1993)
Ma, J., Zhao, Y., Ahalt, S.: OSU SVM Classifier Matlab Toolbox (version 3.00), Ohio State University, Columbus, USA (2002), http://www.ece.osu.edu/~maj/osusvm/
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Li, W., Shi, T., Ding, K. (2006). Gear Crack Detection Using Kernel Function Approximation. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_59
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DOI: https://doi.org/10.1007/11893295_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
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