{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T09:16:18Z","timestamp":1768727778524,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073213"],"award-info":[{"award-number":["62073213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YBR016"],"award-info":[{"award-number":["2022YBR016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52205111"],"award-info":[{"award-number":["52205111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Maritime University Graduate Student Training Program for Top Innovative Talents","award":["62073213"],"award-info":[{"award-number":["62073213"]}]},{"name":"Shanghai Maritime University Graduate Student Training Program for Top Innovative Talents","award":["2022YBR016"],"award-info":[{"award-number":["2022YBR016"]}]},{"name":"Shanghai Maritime University Graduate Student Training Program for Top Innovative Talents","award":["52205111"],"award-info":[{"award-number":["52205111"]}]},{"name":"National Natural Science Foundation Youth Science Foundation Project","award":["62073213"],"award-info":[{"award-number":["62073213"]}]},{"name":"National Natural Science Foundation Youth Science Foundation Project","award":["2022YBR016"],"award-info":[{"award-number":["2022YBR016"]}]},{"name":"National Natural Science Foundation Youth Science Foundation Project","award":["52205111"],"award-info":[{"award-number":["52205111"]}]},{"name":"Opening Project of Guangdong Provincial Key Lab of Robotics and Intelligent System","award":["62073213"],"award-info":[{"award-number":["62073213"]}]},{"name":"Opening Project of Guangdong Provincial Key Lab of Robotics and Intelligent System","award":["2022YBR016"],"award-info":[{"award-number":["2022YBR016"]}]},{"name":"Opening Project of Guangdong Provincial Key Lab of Robotics and Intelligent System","award":["52205111"],"award-info":[{"award-number":["52205111"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Federated learning (FL) is an effective method when a single client cannot provide enough samples for multiple condition fault diagnosis of bearings since it can combine the information provided by multiple clients. However, some of the client\u2019s working conditions are different; for example, different clients are in different stages of the whole life cycle, and different clients have different loads. At this point, the status of each client is not equal, and the traditional FL approach will lead to some clients\u2019 useful information being ignored. The purpose of this paper is to investigate a multiscale recursive FL framework that makes the server more focused on the useful information provided by the clients to ensure the effectiveness of FL. The proposed FL method can build reliable multiple working condition fault diagnosis models due to the increased focus on useful information in the FL process and the full utilization of server information through local multiscale feature fusion. The validity of the proposed method was verified with the Case Western Reserve University benchmark dataset. With less local client training data and complex fault types, the proposed method improves the accuracy of fault diagnosis by 23.21% over the existing FL fault diagnosis.<\/jats:p>","DOI":"10.3390\/e25081165","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T09:27:48Z","timestamp":1691141268000},"page":"1165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Multiscale Recursive Attention Gate Federation Method for Multiple Working Conditions Fault Diagnosis"],"prefix":"10.3390","volume":"25","author":[{"given":"Zhiqiang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Funa","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoge","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglin","family":"Wen","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"School of AutoMation, Guangdong University of Petrochemical Technology, Maoming 525000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiong","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7525-8466","authenticated-orcid":false,"given":"Tianzhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, S., Cao, R., Xu, D., and Fan, Y. 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