Abstract
Based on the research of motor rolling bearings of the urban rail train, we propose a kind of multi-resolution state domain (MRSD) method to identify the working condition of the rolling bearing and apply principal component analysis (PCA) method on fault classification. First, the preprocessed signal is decomposed by wavelet packet algorithm to obtain wavelet packet coefficients, and then the correlation coefficient with the original signal is calculated. On the foundation of the correlation coefficient, multi-resolution correlation entropies (MRCE) are extracted as feature parameters. Then, we utilize PCA approach to get Descartes set which represents bearing status, that MRSD, to classify the different-sourced motor bearing condition intelligently. Finally, the method is certified effectively and feasibly by the experimental result to discriminate the state of train motor bearings.
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Acknowledgements
The authors would like to give appreciation to the editor and anonymous reviewers. This work was supported by National Key Research and Development Program of China (No. 2016YFB1200505).
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Chen, X., Jia, L., Fu, Y., Qin, Y. (2018). Application of Multi-resolution State Domain Method in State Identification of Train Motor Rolling Bearings. 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_16
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DOI: https://doi.org/10.1007/978-981-10-7989-4_16
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