Abstract
The data preprocessing, feature extraction, classifier training and testing play as the key components in a typical fault diagnosis system. This paper proposes a new application of extreme learning machines (ELM) in an integrated manner, where multiple ELM layers play correspondingly different roles in the fault diagnosis framework. The ELM based representational learning framework integrates functions including data preprocessing, feature extraction and dimension reduction. In the novel framework, an ELM based autoencoder is trained to get a hidden layer output weight matrix, which is then used to transform the input data into a new feature representation. Finally, a single layered ELM is applied for fault classification. Compared with existing feature extraction methods, the output weight matrix is treated as the mapping result with weighted distribution of input vector. It avoids wiping off “insignificant” feature information that may convey some undiscovered knowledge. The proposed representational learning framework does not need parameters fine-tuning with iterations. Therefore, the training speed is much faster than the traditional back propagation-based DL or support vector machine method. The experimental tests are carried out on a wind turbine generator simulator, which demonstrates the advantages of this method in both speed and accuracy.
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References
Wong, P.K., Yang, Z., Vong, C.M., Zhong, J.: Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Neurocomputing 128, 249–257 (2014)
Bianchi, D., Mayrhofer, E., Grschl, M., Betz, G., Vernes, A.: Wavelet packet transform for detection of single events in acoustic emission signals. In: Mechanical Systems and Signal Processing (2015)
Keskes, H., Braham, A., Lachiri, Z.: Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet svm. Electr. Power Syst. Res. 97, 151–157 (2013)
Ebrahimi, F., Setarehdan, S.-K., Ayala-Moyeda, J., Nazeran, H.: Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals. Comput. Methods Programs Biomed. 112(1), 47–57 (2013)
Allen, E.A., Erhardt, E.B., Wei, Y., Eichele, T., Calhoun, V.D.: Capturing inter-subject variability with group independent component analysis of fmri data: a simulation study. Neuroimage 59(4), 4141–4159 (2012)
Du, K.-L., Swamy, M.: Independent Component Analysis, pp. 419–450. Springer, London (2014)
Tang, J., Deng, C., Huang, G.-B.: Extreme learning machine for multilayer perceptron (2015)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103. ACM (2008)
Yang, Z., Wong, P.K., Vong, C.M., Zhong, J., Liang, J.: Simultaneous-fault diagnosis of gas turbine generator systems using a pairwise-coupled probabilistic classifier. Math. Prob. Eng. 2013 (2013)
Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 513–529 (2012)
Huang, G.-B., Ding, X., Zhou, H.: Optimization method based extreme learning machine for classification. Neurocomputing 74(1), 155–163 (2010)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)
Luo, J., Vong, C.-M., Wong, P.-K.: Sparse bayesian extreme learning machine for multi-classification. IEEE Trans. Neural Networks Learn. Syst. 25(4), 836–843 (2014)
Cambria, E., Huang, G.-B., Kasun, L.L.C., Zhou, H., Vong, C.M., Lin, J., Yin, J., Cai, Z., Liu, Q., Li, K., et al.: Extreme learning machines [trends & controversies]. IEEE Intell. Syst. 28(6), 30–59 (2013)
Bartlett, P.L.: The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans. Inf. Theory 44(2), 525–536 (1998)
Acknowledgments
The authors would like to thank the University of Macau for funding support under Grants MYRG2015-00077-FST.
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Yang, Z., Wang, X., Wong, P.K., Zhong, J. (2016). ELM Based Representational Learning for Fault Diagnosis of Wind Turbine Equipment. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_14
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DOI: https://doi.org/10.1007/978-3-319-28373-9_14
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