{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:43:37Z","timestamp":1762325017929,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shandong Province","award":["ZR2018LE014"],"award-info":[{"award-number":["ZR2018LE014"]}]},{"name":"National Natural Science Foundation of China","award":["11702162"],"award-info":[{"award-number":["11702162"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The \u21132-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.<\/jats:p>","DOI":"10.3390\/s21103382","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T22:46:14Z","timestamp":1620859574000},"page":"3382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem"],"prefix":"10.3390","volume":"21","author":[{"given":"Zhongwei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2367-5097","authenticated-orcid":false,"given":"Mingyu","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China"}]},{"given":"Liping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China"}]},{"given":"Sujuan","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0392-1935","authenticated-orcid":false,"given":"Chicheng","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.compchemeng.2018.03.025","article-title":"Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection","volume":"115","author":"Onel","year":"2018","journal-title":"Comput. 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