Abstract
For effective utilization of a large amount of vibration data which are collected during the normal operation of train rolling bearing, this paper puts forward a new method for rolling bearing degradation condition assessment which combines the local mean decomposition (LMD) and support vector data description (SVDD). LMD is used to decompose the vibration signal, after the decomposition, we extract feature vector from three points of view: time–frequency, energy and entropy, statistical characteristic value. Principal component analysis can help to reduce dimension. Therefore, we just need to collect the data when rolling bearing normally operates to establish the evaluation model, and then realize the rolling bearing degradation status quantitative evaluation.
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Acknowledgments
This work is supported by the Research Fund for the Doctoral Program of Higher Education of China under Grant 20120009110035 and theory of mass transit train system reliability and safety assessment (No. I14K00451).
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Wang, D., Qin, Y., Cheng, X., Zhang, Z., Li, H., Deng, X. (2016). Train Rolling Bearing Degradation Condition Assessment Based on Local Mean Decomposition and Support Vector Data Description. In: Qin, Y., Jia, L., Feng, J., An, M., Diao, L. (eds) Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation. Lecture Notes in Electrical Engineering, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49370-0_18
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DOI: https://doi.org/10.1007/978-3-662-49370-0_18
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