{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T03:16:00Z","timestamp":1784949360307,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Education of Jilin Province","award":["JJKH20251092CY"],"award-info":[{"award-number":["JJKH20251092CY"]}]},{"name":"Department of Education of Jilin Province","award":["ZKP202018"],"award-info":[{"award-number":["ZKP202018"]}]},{"name":"Department of Education of Jilin Province","award":["2024JBH01LX6"],"award-info":[{"award-number":["2024JBH01LX6"]}]},{"name":"Changchun University","award":["JJKH20251092CY"],"award-info":[{"award-number":["JJKH20251092CY"]}]},{"name":"Changchun University","award":["ZKP202018"],"award-info":[{"award-number":["ZKP202018"]}]},{"name":"Changchun University","award":["2024JBH01LX6"],"award-info":[{"award-number":["2024JBH01LX6"]}]},{"name":"Changchun Jiamei Machinery Manufacturing Co., Ltd.","award":["JJKH20251092CY"],"award-info":[{"award-number":["JJKH20251092CY"]}]},{"name":"Changchun Jiamei Machinery Manufacturing Co., Ltd.","award":["ZKP202018"],"award-info":[{"award-number":["ZKP202018"]}]},{"name":"Changchun Jiamei Machinery Manufacturing Co., Ltd.","award":["2024JBH01LX6"],"award-info":[{"award-number":["2024JBH01LX6"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Rolling bearing vibration signals are often severely affected by strong external noise, which can obscure fault-related features and hinder accurate diagnosis. To address this challenge, this paper proposes an enhanced Deep Residual Shrinkage Network with Dynamic Convolution and Selective Kernel Attention (DDRSN-SKA). First, one-dimensional vibration signals are converted into two-dimensional time frequency images using the Continuous Wavelet Transform (CWT), providing richer input representations. Then, a dynamic convolution module is introduced to adaptively adjust kernel weights based on the input, enabling the network to better extract salient features. To improve feature discrimination, an Selective Kernel Attention (SKAttention) module is incorporated into the intermediate layers of the network. By applying a multi-receptive field channel attention mechanism, the network can emphasize critical information and suppress irrelevant features. The final classification layer determines the fault types. Experiments conducted on both the Case Western Reserve University (CWRU) dataset and a laboratory-collected bearing dataset demonstrate that DDRSN-SKA achieves diagnostic accuracies of 98.44% and 94.44% under \u22128 dB Gaussian and Laplace noise, respectively. These results confirm the model\u2019s strong noise robustness and its suitability for fault diagnosis in noisy industrial environments.<\/jats:p>","DOI":"10.3390\/a18090569","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T08:43:54Z","timestamp":1757493834000},"page":"569","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on Deep Residual Shrinkage Network with Dynamic Convolution and Selective Kernel Attention Model"],"prefix":"10.3390","volume":"18","author":[{"given":"Xiaoxu","family":"Li","sequence":"first","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2513-3226","authenticated-orcid":false,"given":"Jixuan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3059-136X","authenticated-orcid":false,"given":"Jiahao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiamin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicle Engineering, Jilin Engineering Normal University, Changchun 130052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaming","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuelian","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.ymssp.2013.06.004","article-title":"Prognostics and health management design for rotary machinery systems\u2014Reviews, methodology and applications","volume":"42","author":"Lee","year":"2014","journal-title":"Mech. 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