{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T05:35:32Z","timestamp":1762061732527,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:00:00Z","timestamp":1660521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFB1710600","2021C01111","62171170"],"award-info":[{"award-number":["2020YFB1710600","2021C01111","62171170"]}]},{"name":"Key Research Development Program of Zhejiang Province","award":["2020YFB1710600","2021C01111","62171170"],"award-info":[{"award-number":["2020YFB1710600","2021C01111","62171170"]}]},{"name":"National Natural Science Foundation of China","award":["2020YFB1710600","2021C01111","62171170"],"award-info":[{"award-number":["2020YFB1710600","2021C01111","62171170"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Domain adaptation-based bearing fault diagnosis methods have recently received high attention. However, the extracted features in these methods fail to adequately represent fault information due to the versatility of the work scenario. Moreover, most existing adaptive methods attempt to align the feature space of domains by calculating the sum of marginal distribution distance and conditional distribution distance, without considering variable cross-domain diagnostic scenarios that provide significant cues for fault diagnosis. To address the above problems, we propose a deep convolutional multi-space dynamic distribution adaptation (DCMSDA) model, which consists of two core components: two feature extraction modules and a dynamic distribution adaptation module. Technically, a multi-space structure is proposed in the feature extraction module to fully extract fault features of the marginal distribution and conditional distribution. In addition, the dynamic distribution adaptation module utilizes different metrics to capture distribution discrepancies, as well as an adaptive coefficient to dynamically measure the alignment proportion in complex cross-domain scenarios. This study compares our method with other advanced methods, in detail. The experimental results show that the proposed method has excellent diagnosis performance and generalization performance. Furthermore, the results further demonstrate the effectiveness of each transfer module proposed in our model.<\/jats:p>","DOI":"10.3390\/e24081122","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T20:58:08Z","timestamp":1660597088000},"page":"1122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Transfer Network with Multi-Space Dynamic Distribution Adaptation for Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"given":"Xiaorong","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaojian","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1355-2619","authenticated-orcid":false,"given":"Caiming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3661-5560","authenticated-orcid":false,"given":"Jiahao","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7264-2019","authenticated-orcid":false,"given":"Zhiwei","family":"He","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyu","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1109\/TIE.2016.2627020","article-title":"Deep Model Based Domain Adaptation for Fault Diagnosis","volume":"64","author":"Lu","year":"2016","journal-title":"IEEE Trans. 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