{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T19:46:04Z","timestamp":1772653564933,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T00:00:00Z","timestamp":1719014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Science and Technology Project","award":["KZS2101030"],"award-info":[{"award-number":["KZS2101030"]}]},{"name":"Zhejiang Provincial Science and Technology Project","award":["KZ1410067"],"award-info":[{"award-number":["KZ1410067"]}]},{"name":"National Natural Science Foundation of China","award":["KZS2101030"],"award-info":[{"award-number":["KZS2101030"]}]},{"name":"National Natural Science Foundation of China","award":["KZ1410067"],"award-info":[{"award-number":["KZ1410067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In response to the issues of low model recognition accuracy and weak generalization in mechanical equipment fault diagnosis due to scarce data, this paper proposes an innovative solution, a cross-device secondary transfer-learning method based on EGRUN (efficient gated recurrent unit network). This method utilizes continuous wavelet transform (CWT) to transform source domain data into images. The EGRUN model is initially trained, and shallow layer weights are frozen. Subsequently, random overlapping sampling is applied to the target domain data to enhance data and perform secondary transfer learning. The experimental results demonstrate that this method not only significantly improves the model\u2019s ability to learn fault features but also enhances its classification accuracy and generalization performance. Compared to current state-of-the-art algorithms, the model proposed in this study shows faster convergence speed, higher diagnostic accuracy, and superior robustness and generalization, providing an effective approach to address the challenges arising from scarce data and varying operating conditions in practical engineering scenarios.<\/jats:p>","DOI":"10.3390\/s24134070","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T06:59:58Z","timestamp":1719212398000},"page":"4070","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Intelligent Fault Diagnosis Method Based on Cross-Device Secondary Transfer Learning of Efficient Gated Recurrent Unit Network"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1681-3163","authenticated-orcid":false,"given":"Chaoquan","family":"Mo","sequence":"first","affiliation":[{"name":"College of Mechanical & Electrical Engineering, Wenzhou University, Wenzhou 325035, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1206-5317","authenticated-orcid":false,"given":"Ke","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Mechanical & Electrical Engineering, Wenzhou University, Wenzhou 325035, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"234","DOI":"10.3901\/JME.2023.21.234","article-title":"Application of Harmonic Characteristic Mode Decomposition Method in Bearing Fault Diagnosis","volume":"59","author":"Miao","year":"2023","journal-title":"J. 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