{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T12:34:21Z","timestamp":1777898061481,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T00:00:00Z","timestamp":1721174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["2022B02038"],"award-info":[{"award-number":["2022B02038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gearbox fault diagnosis is essential in the maintenance and preventive repair of industrial systems. However, in actual working environments, noise frequently interferes with fault signals, consequently reducing the accuracy of fault diagnosis. To effectively address this issue, this paper incorporates the noise attenuation of the DRSN-CW model. A compound fault detection method for gearboxes, integrated with a cross-attention module, is proposed to enhance fault diagnosis performance in noisy environments. First, frequency domain features are extracted from the public dataset by using the fast Fourier transform (FFT). Furthermore, the cross-attention mechanism model is inserted in the optimal position to improve the extraction and recognition rate of global and local fault features. Finally, noise-related features are filtered through soft thresholds within the network structure to efficiently mitigate noise interference. The experimental results show that, compared to existing network models, the proposed model exhibits superior noise immunity and high-precision fault diagnosis performance.<\/jats:p>","DOI":"10.3390\/s24144633","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T15:15:19Z","timestamp":1721229319000},"page":"4633","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Gearbox Fault Diagnosis Method in Noisy Environments Based on Deep Residual Shrinkage Networks"],"prefix":"10.3390","volume":"24","author":[{"given":"Jianhui","family":"Cao","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinze","family":"Jiao","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peibo","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baobao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Software, Xinjiang University, Urumqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymssp.2013.09.015","article-title":"Multiwavelet Transform and Its Applications in Mechanical Fault Diag-nosis\u2014A Review","volume":"43","author":"Sun","year":"2014","journal-title":"Mech. 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