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Therefore, it is difficult to effectivelyextract the fault features. In this paper, a fault feature extraction method based on improved VMD multi-scale dispersion entropy and TVD-CYCBD is proposed. First, the marine predator algorithm (MPA) is used to optimize the modal components and penalty factors in VMD. Second, the optimized VMD is used to model and decompose the fault signal, and then the optimal signal components are filtered according to the combined weight index criteria. Third, TVD is used to denoise the optimal signal components. Finally, CYCBD filters the de-noised signal and then envelope demodulation analysis is carried out. Through the simulation signal experiment and the actual fault signal experiment, the results verified that multiple frequency doubling peaks can be seen from the envelope spectrum, and there is little interference near the peak, which shows the good performance of the method.<\/jats:p>","DOI":"10.3390\/e25020277","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T02:21:12Z","timestamp":1675304472000},"page":"277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2211-8718","authenticated-orcid":false,"given":"Jingzong","family":"Yang","sequence":"first","affiliation":[{"name":"School of Dig Data, Baoshan University, Baoshan 678000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4218-105X","authenticated-orcid":false,"given":"Chengjiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information, Yunnan Normal University, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuefeng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anning","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Dig Data, Baoshan University, Baoshan 678000, China"},{"name":"School of Information, Yunnan Normal University, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianqing","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Dig Data, Baoshan University, Baoshan 678000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/OJIES.2020.3046044","article-title":"Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap","volume":"2","author":"Jiang","year":"2020","journal-title":"IEEE Open J. 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