Multi-Domain Extreme Learning Machine for Bearing Failure Detection Based on Variational Modal Decomposition and Approximate Cyclic Correntropy

Author:

Wang XiaohuiORCID,Sui Guangzhou,Xiang JiaweiORCID,Wang GuangbinORCID,Huo ZhiqiangORCID,Huang ZhenORCID

Funder

National Natural Science Foundation of China

Non-Funded Science and Technology Public Relations Plan Project Foundation of Zhanjiang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An IGSA-VMD method for fault frequency identification of cylindrical roller bearing;Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science;2024-05-21

2. Fault Diagnosis Method for Rolling Bearings Based on Grey Relation Degree;Entropy;2024-02-29

3. A novel selective ensemble system for wind speed forecasting: From a new perspective of multiple predictors for subseries;Energy Conversion and Management;2023-10

4. Rolling Bearing Fault Diagnosis across Operating Conditions Based on Unsupervised Domain Adaptation;Lubricants;2023-09-08

5. Bearing Vibration Signal Restoration Method Based on Fuzzy Adaptive Sliding Mode Algorithm;2023 International Conference on Network, Multimedia and Information Technology (NMITCON);2023-09-01

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