{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:33:12Z","timestamp":1778689992422,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Science and Technology Foundation of Guizhou Province","award":["QKHJC-ZK [2021] YB271"],"award-info":[{"award-number":["QKHJC-ZK [2021] YB271"]}]},{"name":"The Science and Technology Foundation of Guizhou Province","award":["QKHZC [2022] YB074"],"award-info":[{"award-number":["QKHZC [2022] YB074"]}]},{"name":"Guizhou Science and Technology Support Project","award":["QKHJC-ZK [2021] YB271"],"award-info":[{"award-number":["QKHJC-ZK [2021] YB271"]}]},{"name":"Guizhou Science and Technology Support Project","award":["QKHZC [2022] YB074"],"award-info":[{"award-number":["QKHZC [2022] YB074"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Recent deep-learning methods for fault diagnosis of rolling bearings need a significant amount of computing time and resources. Most of them cannot meet the requirements of real-time fault diagnosis of rolling bearings under the cloud computing framework. This paper proposes a quick cloud\u2013edge collaborative bearing fault diagnostic method based on the tradeoff between the advantages and disadvantages of cloud and edge computing. First, a collaborative cloud-based framework and an improved DSCNN\u2013GAP algorithm are suggested to build a general model using the public bearing fault dataset. Second, the general model is distributed to each edge node, and a limited number of unique fault samples acquired by each edge node are used to quickly adjust the parameters of the model before running diagnostic tests. Finally, a fusion result is made from the diagnostic results of each edge node by DS evidence theory. Experiment results show that the proposed method not only improves diagnostic accuracy by DSCNN\u2013GAP and fusion of multi-sensors, but also decreases diagnosis time by migration learning with the cloud\u2013edge collaborative framework. Additionally, the method can effectively enhance data security and privacy protection.<\/jats:p>","DOI":"10.3390\/e24091277","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T22:53:46Z","timestamp":1663023226000},"page":"1277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud\u2013Edge Collaboration"],"prefix":"10.3390","volume":"24","author":[{"given":"Xianghong","family":"Tang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gongsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1109\/TIE.2017.2762623","article-title":"Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines","volume":"65","author":"Lucio","year":"2018","journal-title":"IEEE Trans. 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