{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:24:55Z","timestamp":1778779495864,"version":"3.51.4"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guizhou Science and Technology Support Programmer (project title: Research on Intelligent Assembly Technology of Pyrotechnics Driven by Digital Twins","award":["[2023] 309"],"award-info":[{"award-number":["[2023] 309"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In recent years, researchers have extensively studied deep learning to diagnose faults in rotating machinery. However, existing methods often fail to adequately capture and express fault information. The absence of sensitive features limits the model\u2019s ability to extract discriminative characteristics, particularly in environments where signals are subject to substantial noise interference or where fault samples are scarce. To address the aforementioned issues, this paper proposes a fault diagnosis method based on multi-scale wavelet-weight initialization and an adaptive gain mechanism (MSWAG). This method first employs multiple wavelet basis functions to initialize (MWTI) the convolutional kernel weights in the neural network\u2019s first layer. By utilizing a channel-wise convolutional architecture, it integrates the complementary information extracted by different wavelets during feature extraction. Secondly, an adaptive gain mechanism (AGM) is introduced that automatically learns a scaling factor for feature amplitudes, thereby achieving nonlinear enhancement of key features and noise suppression. Finally, a multi-scale feature extractor (MSFE) is constructed to comprehensively capture discriminative information within fault signals by adapting perception strategies to different frequency components. Experimental results based on real rotating machinery data demonstrate that MSWAG exhibits superior diagnostic performance under both intense noise and sparse sample conditions, significantly outperforming existing mainstream methods. This finding highlights the method\u2019s potential for enhancing key features and for practical applications.<\/jats:p>","DOI":"10.1093\/jcde\/qwag018","type":"journal-article","created":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T12:34:00Z","timestamp":1772368440000},"page":"262-282","source":"Crossref","is-referenced-by-count":1,"title":["A multi-scale fault diagnosis method for rotating machinery with multi-wavelet weight initialization and adaptive gain mechanism"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0849-0364","authenticated-orcid":false,"given":"Yunjin","family":"Hu","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University , Guiyang 550028 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5871-7213","authenticated-orcid":false,"given":"Xudong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University , Guiyang 550028 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0005-4653","authenticated-orcid":false,"given":"Qingsheng","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University , Guiyang 550028 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3750-6537","authenticated-orcid":false,"given":"Haisong","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University , Guiyang 550028 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University , Guiyang 550028 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"2026032602201113200_bib1","doi-asserted-by":"publisher","first-page":"2083","DOI":"10.1016\/j.ymssp.2011.01.017","article-title":"Vibration analysis of rotating machinery using time\u2013frequency analysis and wavelet techniques","volume":"25","author":"Al-Badour","year":"2011","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2026032602201113200_bib2","doi-asserted-by":"publisher","first-page":"9878228","DOI":"10.1155\/2023\/9878228","article-title":"Application of time-frequency analysis in rotating machinery fault diagnosis","volume":"2023","author":"Bai","year":"2023","journal-title":"Shock and Vibration"},{"key":"2026032602201113200_bib3","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1016\/j.ymssp.2011.08.002","article-title":"Early fault diagnosis of rotating machinery based on wavelet packets\u2014Empirical mode decomposition feature extraction and neural network","volume":"27","author":"Bin","year":"2012","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2026032602201113200_bib4","doi-asserted-by":"publisher","first-page":"108105","DOI":"10.1016\/j.ymssp.2021.108105","article-title":"An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery","volume":"163","author":"Brito","year":"2022","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2026032602201113200_bib5","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1093\/jcde\/qwaf080","article-title":"WCFormer: A wavelet-enhanced CNN-transformer hybrid network for bearing fault diagnosis using multi-sensor signal fusion","volume":"12","author":"Cao","year":"2025","journal-title":"Journal of Computational Design and Engineering"},{"key":"2026032602201113200_bib6","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1093\/jcde\/qwae052","article-title":"PCDC: Prototype-assisted dual-contrastive learning with depthwise separable convolutional neural network for few-shot fault diagnosis of permanent magnet synchronous motors under new operating conditions","volume":"11","author":"Chae","year":"2024","journal-title":"Journal of Computational Design and Engineering"},{"key":"2026032602201113200_bib7","doi-asserted-by":"publisher","first-page":"6:1","DOI":"10.1145\/1883612.1883613","article-title":"Discrete wavelet transform-based time series analysis and mining","volume":"43","author":"Chaovalit","year":"2011","journal-title":"ACM Computing Survey"},{"key":"2026032602201113200_bib8","doi-asserted-by":"crossref","unstructured":"Chen S., Liu Z., He X., Zou D., Zhou D. 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