Feature Mapping Techniques for Improving the Performance of Fault Diagnosis of Synchronous Generator
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Published
Nov 3, 2020
R. Gopinath
C. Santhosh Kumar
K. Vishnuprasad
K. I. Ramachandran
Abstract
Support vector machine (SVM) is a popular machine learning algorithm used extensively in machine fault diagnosis. In this paper, linear, radial basis function (RBF), polynomial, and sigmoid kernels are experimented to diagnose inter-turn faults in a 3kVA synchronous generator. From the preliminary results, it is observed that the performance of the baseline system
is not satisfactory since the statistical features are nonlinear and does not match to the kernels used. In this work, the features are linearized to a higher dimensional space to improve the performance of fault diagnosis system for a synchronous generator using feature mapping techniques, sparse coding and locality constrained linear coding (LLC). Experiments and results show that LLC is superior to sparse coding for improving the performance of fault diagnosis of a synchronous generator. For the balanced data set, LLC improves the overall fault identification accuracy of the baseline RBF system by 22.56%, 18.43% and 17.05% for the R, Y and Bphase faults respectively.
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Keywords
Machine fault diagnosis, Synchronous generator, Support Vector Machine, kernels, Sparse coding, Locality Constrained Linear Coding
References
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Yang, J., Yu, K., Gong, Y., & Huang, T. (2009). Linear spatial pyramid matching using sparse coding for image classification. IEEE Computer Vision and Pattern Recognition conference, 1794-1801.
Yang, Y., Liao, Y., Meng, G., & Lee, J. (2011). A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis. Expert Systems with Applications, 38(9), 11311-11320.
Yu, K., Zhang, T., & Gong, Y. (2009). Nonlinear learning using local coordinate coding. Advances in neural information processing systems, 2223-2231.
Zhang, K., Li, Y., Scarf, P., & Ball, A. (2011). Feature selection for high-dimensional machinery fault diagnosis data using multiple models and radial basis function networks. Neurocomputing, 74(17), 2941-2952.
Zhang, P., Wee, C., Niethammer, M., Shen, D., & Yap, P. (2013). Large deformation image classification using generalized locality-constrained linear coding. In Medical image computing and computer-assisted intervention-miccai 2013 (p. 292-299). Springer.
Zhang, Y. (2009). Enhanced statistical analysis of non-linear processes using kpca, kica and svm. Chemical Engineering Science, 64(5), 801-811.
Zhou, J., Shi, J., & Li, G. (2011). Fine tuning support vector machines for short-term wind speed forecasting. Energy Conversion and Management, 52(4), 1990-1998.
Zhou, X., Cui, N., Li, Z., Liang, F., & Huang, T. S. (2009, September). Hierarchical gaussianization for image classification. IEEE Conference on Computer Vision, 1971-1977.
Zhu, H., Wang, X., Zhao, Y., Li, Y., Wang, W., & Li, L. (2014). Sparse representation based on adaptive multiscale features for robust machinery fault diagnosis. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.
Chen, Y. D., Du, R., & Qu, L. S. (1995). Fault features of large rotating machinery and diagnosis using sensor fusion. Journal of Sound and Vibration, 188(2), 227-242.
Chiang, L. H., Kotanchek, M. E., & Kordon, A. K. (2004). Fault diagnosis based on fisher discriminant analysis and support vector machines. Computers & chemical engineering, 28(8), 1389-1401.
Deng, S., Jing, B., & Zhou, H. (2014). Fusion sparse coding algorithm for impulse feature extraction in machinery weak fault detection. In Prognostics and system health management conference (phm-2014 hunan) (p. 251-256).
Fu, M., Tian, Y., & Wu, F. (2015). Step-wise support vector machines for classification of overlapping samples. Neurocomputing, 155, 159-166.
Gopinath, R., Nambiar, T. N. P., Abhishek, S., Pramodh, S. M., Pushparajan, M., Ramachandran, K. I., . . . Thirugnanam, R. (2013). Fault injection capable synchronous generator for condition based maintenance. In Intelligent systems and control (isco), 2013 7th international conference on ieee (p. 60-64).
He, Q., Liu, Y., & Kong, F. (2011). Machine fault signature analysis by midpoint-based empirical mode decomposition. Measurement Science and Technology, 22(1), 015702.
Japkowicz, N., & Shah, M. (2011). Evaluating learning algorithms: a classification perspective. Cambridge University Press.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition based maintenance. Mechanical systems and signal processing, 1483-1510.
Kang, M., Kim, J., Kim, J., Tan, A., Kim, E. Y., & Choi, B. (2015). Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis. Power Electronics, IEEE Transactions on, 30(5), 2786-2797.
Kuhn, H. W., & Tucker, A. W. (1951). Nonlinear programming. In Proceedings of the second berkeley symposium on mathematical statistics and probability (p. 481-492). University of California Press.
Lei, Y., He, Z., & Zi, Y. (2008). A new approach to intelligent fault diagnosis of rotating machinery. expert systems with applications. Expert Systems with Applications, 1593-1600.
Liang, Y., Song, M., Bu, J., & Chen, C. (2014). Colorization for gray scale facial image by locality-constrained linear coding. Journal of Signal Processing Systems, 74(1), 59-67.
Lin, J., & Zuo, M. J. (2004). Extraction of periodic components for gearbox diagnosis combining wavelet filtering and cyclostationary analysis. Journal of vibration and acoustics, 126(3), 449-451.
Liu, H., Liu, C., & Huang, Y. (2011). Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mechanical Systems and Signal Processing, 25(2), 558-574.
Lu, S., Wang, Z., Mei, T., Guan, G., & Feng, D. D. (2014). A bag-of-importance model with locality-constrained coding based feature learning for video summarization. Multimedia, IEEE Transactions on IEEE, 16(6), 1497-1509.
Nayak, J., Naik, B., & Behera, H. S. (2015). A comprehensive survey on support vector machine in data mining tasks: Applications and challenges. International Journal of Database Theory and Application, 8(1), 169-186.
Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: A strategy employed by v1? Vision research, 37(23), 3311-3325.
Peter, W. T., Peng, Y. H., & Yam, R. (2001). Wavelet analysis and envelope detection for rolling element bearing fault diagnosis their effectiveness and flexibilities. Journal of Vibration and Acoustics, 123(3), 303-310.
Rahmani, H., Mahmood, A., Huynh, D., & Mian, A. (2014). Action classification with locality-constrained linear coding. In Pattern recognition (icpr), 2014 22nd international conference on ieee (p. 3511-3516).
Samanta, B., & Al-Balushi, K. R. (2003). Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical systems and signal processing, 17(2), 317-328.
Saxena, A., Wu, B., & Vachtsevanos, G. (2005, June). A methodology for analyzing vibration data from planetary gear systems using complex morlet wavelets. Proceedings of IEEE American Control Conference, 4730-4735.
Vapnik, V. N., & Vapnik, V. (1998). Statistical learning theory (Vol. 2). New York: Wiley.
Verron, S., Tiplica, T., & Kobi, A. (2008). Fault detection and identification with a new feature selection based on mutual information. Journal of Process Control, 18(5), 479-490.
Wang, B., Gai, W., Guo, S., Liu, Y., Wang, W., & Zhang, M. (2014). Spatially regularized and locality-constrained linear coding for human action recognition. Optical Review, 21(3), 226-236.
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010). Locality-constrained linear coding for image classification. IEEE Computer Vision and Pattern Recognition (CVPR) conference, 3360-3367.
Wang, X., & Pardalos, P. M. (2015). A survey of support vector machines with uncertainties. Annals of Data Science, 1(3-4), 293-309.
Widodo, A., & Yang, B. S. (2007). Application of non-linear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Systems with Applications, 33(1), 241-250.
Widodo, A., Yang, B. S., & Han, T. (2007). Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Systems with Applications, 32(2), 299-312.
Wu, B., Saxena, A., Khawaja, T. S., Patrick, R., Vachtsevanos, G., & Sparis, P. (2004, September). An approach to fault diagnosis of helicopter planetary gears. Proceedings of IEEE AUTOTESTCON, 475-481.
Wu, B., Saxena, A., Patrick, R., & Vachtsevanos, G. (2005, July). Vibration monitoring for fault diagnosis of helicopter planetary gears. In Proceedings of 16th IFAC World Congress.
Yan, R., & Gao, R. X. (2008). Rotary machine health diagnosis based on empirical mode decomposition. Journal of Vibration and Acoustics, 130(2), 21007.
Yan, R., Gao, R. X., & Wang, C. (2009). Experimental evaluation of a unified time-scale-frequency technique for bearing defect feature extraction. Journal of Vibration and Acoustics, 131(4), 041012.
Yang, J., Yu, K., Gong, Y., & Huang, T. (2009). Linear spatial pyramid matching using sparse coding for image classification. IEEE Computer Vision and Pattern Recognition conference, 1794-1801.
Yang, Y., Liao, Y., Meng, G., & Lee, J. (2011). A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis. Expert Systems with Applications, 38(9), 11311-11320.
Yu, K., Zhang, T., & Gong, Y. (2009). Nonlinear learning using local coordinate coding. Advances in neural information processing systems, 2223-2231.
Zhang, K., Li, Y., Scarf, P., & Ball, A. (2011). Feature selection for high-dimensional machinery fault diagnosis data using multiple models and radial basis function networks. Neurocomputing, 74(17), 2941-2952.
Zhang, P., Wee, C., Niethammer, M., Shen, D., & Yap, P. (2013). Large deformation image classification using generalized locality-constrained linear coding. In Medical image computing and computer-assisted intervention-miccai 2013 (p. 292-299). Springer.
Zhang, Y. (2009). Enhanced statistical analysis of non-linear processes using kpca, kica and svm. Chemical Engineering Science, 64(5), 801-811.
Zhou, J., Shi, J., & Li, G. (2011). Fine tuning support vector machines for short-term wind speed forecasting. Energy Conversion and Management, 52(4), 1990-1998.
Zhou, X., Cui, N., Li, Z., Liang, F., & Huang, T. S. (2009, September). Hierarchical gaussianization for image classification. IEEE Conference on Computer Vision, 1971-1977.
Zhu, H., Wang, X., Zhao, Y., Li, Y., Wang, W., & Li, L. (2014). Sparse representation based on adaptive multiscale features for robust machinery fault diagnosis. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.
Section
Technical Papers