{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:20:22Z","timestamp":1778800822659,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2019CFB133"],"award-info":[{"award-number":["2019CFB133"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["51975433"],"award-info":[{"award-number":["51975433"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["51975430"],"award-info":[{"award-number":["51975430"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2019CFB133"],"award-info":[{"award-number":["2019CFB133"]}]},{"name":"National Natural Science Foundation of China","award":["51975433"],"award-info":[{"award-number":["51975433"]}]},{"name":"National Natural Science Foundation of China","award":["51975430"],"award-info":[{"award-number":["51975430"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the problems of early weak fault feature extraction of bearings in rotating machinery, an improved stochastic resonance (SR) is proposed combined with the advantage of SR to enhance weak characteristic signals with noise energy. Firstly, according to the characteristics of the large parameters of the actual fault signal, the amplitude transform coefficient and frequency transform coefficient are introduced to convert the large parameter signal into small parameter signal which can be processed by SR, and the relationship of second-order parameters are introduced. Secondly, a comprehensive evaluation index (CEI) consisted of power spectrum kurtosis, correlation coefficient, structural similarity, root mean square error, and approximate entropy, is constructed through BP neural network. Moreover, this CEI is adopted as fitness function to search the optimal damping coefficient and amplitude transform coefficient with adaptive weight particle swarm optimization (PSO). Finally, according to the improved optimal SR system, the weak fault feature can be extracted. The simulation and experiment verify the effectiveness of the proposed method compared with traditional second-order general scale transform adaptive SR.<\/jats:p>","DOI":"10.3390\/s22176644","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6644","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Weak Fault Feature Extraction Method Based on Improved Stochastic Resonance"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhen","family":"Yang","sequence":"first","affiliation":[{"name":"Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengxing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yajie","family":"Ma","sequence":"additional","affiliation":[{"name":"Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baokang","family":"Yan","sequence":"additional","affiliation":[{"name":"Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4024","DOI":"10.3390\/s21124024","article-title":"Data-driven fault diagnosis for electric drives: A review","volume":"21","author":"David","year":"2021","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.ymssp.2018.06.032","article-title":"A review of stochastic resonance in rotating machine fault detection","volume":"116","author":"Lu","year":"2019","journal-title":"Mech. 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