A novel Bayesian Least Squares Support Vector Machine based Anomaly Detector for Fault Diagnosis
Anomaly detection is the identification of abnormal system behavior, in which a model of normality is constructed, with deviations from the model identified as “abnormal”. Complex high-integrity systems typically operate normally for the majority of their service lives, and so examples of abnormal data may be rare in comparison to the amount of available normal data. Anomaly detection is particularly suited for Intelligent Fault diagnosis of such systems since it allows previously-unseen or poorly- understood modes of failure to be correctly identified. In this paper, we propose a novel Least Squares Support Vector Machine (LSSVM) based Anomaly Detector for efficiently and accurately detecting imminent faults in complex non-linear systems. The Anomaly Detector is supplemented with a Bayesian Inference Framework in order to allow for a probabilistic interpretation of the classification results. Experiments conducted on data from real test cases discussing crack growth on a planetary gearplate on board a UH-60 BlackHawk Aircraft and bending fan blades aboard a chiller show that the Bayesian LSSVM (B-LSSVM) Anomaly Detector can give high identification rates for both the prescribed ‘unknown’ fault samples and the known fault samples.
How to Cite
anomaly detection, Bayesian reasoning, detection, diagnosis
Markou, M., Singh, S. (2003), Novelty Detection: A Review-part 1: Statistical Approaches, Signal Processing, 83 (12): 2481-2497
Markou, M., Singh, S. (2003), Novelty Detection: A Review-part 2: Neural Network based Approaches, Signal Processing, 83 (12): 2499- 2521
Stefano, C. De, Sansone, C., Vento, M. ((2000)), To Reject or Not to Reject: That is The Questionan Answer in Case of Neural Classifiers, IEEE Trans. Systems Man Cybernetics-Part C, 30 (1) 84-94
Tax, M. J., Duin, P. W. (2004), Support Vector Data Description, Machine Learning, 54: 45-66
Dehmeshki, J., et al. (2004), Classification of Lung Data by Sampling and Support Vector Machine, in Engineering Medicine and Biology Society, EMBC 2004. Conference Proceedings. 26th Annual International Conference of the, p. 3194 - 3197
Soman, K.P ., D.M. Shyam, and P . Madhavdas (2003), Efficient classification and analysis of ischemic heart disease using proximal support vector machines based decision trees, in TENCON, Conference on Convergent Technologies for Asia-Pacific Region. 2003. p. 214-217
Hu, Z., et al. (2005), Fusion of multi-class support vector machines for fault diagnosis, in American Control Conference. Proceedings of the 2005, 1941 - 1945
Schölkopf, B., Smola, A. J., Williamson, R. C., et al. (2000), “New Support V ector Algorithms”, Neural Computation, vol. 5, pp.1207-1245
Pan, M.-Q., et al. (2005), Support vector data description with model selection for condition monitoring, in Machine Learning and Cybernetics, Proceedings of 2005 International Conference on. p. 4315 - 4318
Suykens, J.A.K. and J. Vandewalle (1999), Least Squares Support Vector Machine Classifiers, Neural Processing Letters 9(3): p. 293-300
Van Gestel T., Suykens J.A.K., Lanckriet G., Lambrechts A., De Moor B., Vandewalle J. (2002), “Bayesian Framework for Least Squares Support Vector Machine Classifiers, Gaussian Processes and Kernel Fisher Discriminant Analysis”, Neural Computation, vol. 15, no. 5, pp. 1115-1148., Lirias number: 70607
Boser, B.E., I.M. Guyon, and V.N. Vapnik (1992), A Training Algorithm for Optimal Classifiers, in Proc. Fifth Ann. ACM Worksop Computational Learning Theory, D. Haussler, Editor.p. 144-152
Bishop C.M. (1995), Neural networks for pattern recognition, Oxford University Press
MacKay D.J.C. (1992), “Bayesian Interpolation,” Neural Computation, Vol.4, No.3, pp.415-447
Van Gestel T., Suykens J.A.K., Lanckriet G., Lambrechts A., De Moor B., Vandewalle J. (2000), “A Bayesian Framework for Least Squares Support Vector Machine Classifiers,” Internal Report 00-65, ESAT-SISTA, K.U.Leuven, submitted for publication.
Chen, P.-H., C.-J. Lin, and B. Schölkopf (2005), A tutorial on v-support vector machines, Applied Stochastic Models in Business and Industry, 21(2): p. 111-136.
Wu, B., et al. (2005), Vibration monitoring for fault diagnosis of helicopter planetary gears, International Federation of Automatic Control World Conference
Zhang B., Khawaja T., Patrick R., Vachtsevanos G. (2007), Blind Deconvolution De-noising for Helicopter Vibration Data, American Control Conference, ACC '07.
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