Abstract
Bearings are key components in many mechanical facilities, and the research on fault diagnosis for bearing is of great importance to the safe operation of those facilities. Thus, a method for fault diagnosis based on VMD-SVD and extreme learning machine is proposed in this paper. First, the bearing vibration signal is decomposed into a number of stationary intrinsic mode functions (IMF) by VMD method. Second, the initial feature matrix of each IMF component is decomposed by SVD, and the obtained singular value is used as the eigenvector of the signal. Finally, extreme learning machine is used as the classifier for fault diagnosis. This method’s feasibility and effectiveness have also been verified by experiment.
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Acknowledgements
This research is supported by the National Key Research and Development Programs of China (Nos. 2016YFB1200203 and 2016YFB1200402), as well as the State Key Laboratory of Rail Traffic Control and Safety (Contract Nos. RCS2016ZQ003 and RCS2016ZT018), Beijing Jiaotong University.
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Zhou, Q., Qin, Y., Wang, Z., Jia, L. (2018). Study on Fault Diagnosis for Bearing Based on VMD-SVD and Extreme Learning Machine. In: Jia, L., Qin, Y., Suo, J., Feng, J., Diao, L., An, M. (eds) Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017. EITRT 2017. Lecture Notes in Electrical Engineering, vol 483. Springer, Singapore. https://doi.org/10.1007/978-981-10-7989-4_9
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DOI: https://doi.org/10.1007/978-981-10-7989-4_9
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