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show extremely complicated dynamic frequency characteristics. It is unlikely that a few certain frequency components are used as the representative fault signatures for all working conditions. Aiming at a general solution, this paper proposes an intelligent bearing fault diagnosis method that integrates adaptive variational mode decomposition (AVMD), mode sorting based deep belief network (DBN) and extreme learning machine (ELM). It can adaptively decompose non-stationery vibration signals into temporary frequency components and sort out a set of effective frequency components for online fault diagnosis. For online implementation, a similarity matching method is proposed, which can match the online-obtained frequency-domain fault signatures with the historical fault signatures, and the parameters of AVMD-DBN-ELM model are set to be the same as the most similar case. The proposed method can decompose vibration signals into different modes adaptively and retain effective modes, and it can learn from the idea of an attention mechanism and fuse the results according to the weight of MIV. It also can improve the timeliness of the fault diagnosis. For comprehensive verification of the proposed method, the bearing dataset from the University of Ottawa is used, and some recent methods are repeated for comparative analysis. The results can prove that our proposed method has higher reliability, higher accuracy and higher efficiency.<\/jats:p>","DOI":"10.3390\/s22239369","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T05:03:18Z","timestamp":1669870998000},"page":"9369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["An AVMD-DBN-ELM Model for Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"22","author":[{"given":"Xue","family":"Lei","sequence":"first","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningyun","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"},{"name":"State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cunsong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Manufacturing, Nanjing Tech University, Nanjing 210009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108954","DOI":"10.1016\/j.ymssp.2022.108954","article-title":"The influence of the radial internal clearance on the dynamic response of self-aligning ball bearings","volume":"171","author":"Ambrokiewicz","year":"2022","journal-title":"Mech. 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