Fault Diagnosis Method of Rolling Bearing Based on Variational Mode Decomposition Algorithm of Parameter Optimization and Support Vector Machine
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-6226-4_73
Reference18 articles.
1. Liu, H., Zhou, J., Xu, Y., Zheng, Y., Peng, X., Jiang, W.: Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. Neurocomputing 315, 412–424 (2018)
2. Xu, G., Hou, D., Qi, H., Bo, L.: High-speed train wheel set bearing fault diagnosis and prognostics: a new prognostic model based on extendable useful life. Mech. Syst. Signal Process. 146, 107050 (2021)
3. Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2014)
4. Jiang, X., Shen, C., Shi, J., et al.: Initial center frequency-guided VMD for fault diagnosis of rotating machines. J. Sound Vibr. 435, 36–55 (2018)
5. Lecture Notes in Computer Science;K Dragomiretskiy,2015
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