{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T21:40:43Z","timestamp":1774042843407,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"key Science and Technology Project of Yunnan Province","award":["202102AC080002"],"award-info":[{"award-number":["202102AC080002"]}]},{"name":"key Science and Technology Project of Yunnan Province","award":["202002AC080001"],"award-info":[{"award-number":["202002AC080001"]}]},{"name":"Science and Technology Program of Yunnan Province","award":["202102AC080002"],"award-info":[{"award-number":["202102AC080002"]}]},{"name":"Science and Technology Program of Yunnan Province","award":["202002AC080001"],"award-info":[{"award-number":["202002AC080001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The compound fault acoustic signal of a rolling bearing has the characteristics of a varying noise mixture, a low signal-to-noise ratio (SNR), and nonlinearity, which makes it difficult to separate and extract exactly the fault features of compound fault signals. A fault feature extraction approach combining adaptive variational modal decomposition (AVMD) and improved multiverse optimization (IMVO) algorithm parameterized maximum correlated kurtosis deconvolution (MCKD)\u2014named AVMD-IMVO-MCKD\u2014is proposed. In order to adaptively select the parameters of VMD and MCKD, an adaptive optimization method of VMD is proposed, and an improved multiverse optimization (IMVO) algorithm is proposed to determine the parameters of MCKD. Firstly, the acoustic signal of bearing compound faults is decomposed by AVMD to generate several modal components, and the optimal modal component is selected as the reconstruction signal depending on the minimum information entropy of the modal components. Secondly, IMVO is utilized to select the parameters of MCKD, and then MCKD processing is performed on the reconstructed signal. Finally, the compound fault features of the bearing are extracted by the envelope spectrum. Both simulation analysis and acoustic signal experimental data analysis show that the proposed approach can efficiently extract the acoustic signal fault features of bearing compound faults.<\/jats:p>","DOI":"10.3390\/s22186769","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6769","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Compound Fault Feature Extraction of Rolling Bearing Acoustic Signals Based on AVMD-IMVO-MCKD"],"prefix":"10.3390","volume":"22","author":[{"given":"Shishuai","family":"Wu","sequence":"first","affiliation":[{"name":"Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China"}]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Rolling bearing fault diagnosis with adaptive harmonic kurtosis and improved bat algorithm","volume":"70","author":"Qin","year":"2020","journal-title":"IEEE Trans. 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