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Syst."],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The intelligent fault diagnosis model has made a significant development, whose high-precision results rely on a large amount of labeled data. However, in the actual industrial environment, it is very difficult to obtain a large amount of labeled data. It will make it difficult for the fault diagnosis model to converge with limited labeled industrial data. To address this paradox, we propose a novel unsupervised domain adaptation framework (M-Net) for fault diagnosis of rotating machinery, which only requires unlabeled industrial data. The M-Net will be pretrained using the  labeled data, which can be accessed through the labs. In this stage, we propose a multi-scale feature extractor that can extract and fuse multi-scale features. This operation will generalize the features further. Then, we will align the distribution of the labeled data and unlabeled industrial data using the generator model based on multi-kernel maximum mean discrepancy. This will reduce the distribution distance between the labeled data and the unlabeled industrial data. For now, the unsupervised domain adaptation problem has shifted to a semi-supervised domain adaptation problem. The results, obtained through experimental comparison, demonstrate that the M-Net can achieve an accuracy of over 99.99% with labeled data and a maximum transfer accuracy of over 99% with unlabeled industrial data.<\/jats:p>","DOI":"10.1007\/s40747-023-01320-z","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T18:02:44Z","timestamp":1706205764000},"page":"3259-3272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["M-Net: a novel unsupervised domain adaptation framework based on multi-kernel maximum mean discrepancy for fault diagnosis of rotating machinery"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6660-8762","authenticated-orcid":false,"given":"Shihang","family":"Yu","sequence":"first","affiliation":[]},{"given":"Limei","family":"Song","sequence":"additional","affiliation":[]},{"given":"Shanchen","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Min","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"He","sequence":"additional","affiliation":[]},{"given":"Pengfei","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"1320_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.109789","volume":"185","author":"S Yu","year":"2023","unstructured":"Yu S, Wang M, Pang S, Song L, Zhai X, Zhao Y (2023) TDMSAE: A transferable decoupling multi-scale autoencoder for mechanical fault diagnosis. 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