{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:51:20Z","timestamp":1777503080943,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009110","name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2022D01C390"],"award-info":[{"award-number":["2022D01C390"]}],"id":[{"id":"10.13039\/100009110","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009110","name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2022B02016-1"],"award-info":[{"award-number":["2022B02016-1"]}],"id":[{"id":"10.13039\/100009110","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["2022D01C390"],"award-info":[{"award-number":["2022D01C390"]}]},{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["2022B02016-1"],"award-info":[{"award-number":["2022B02016-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To tackle the issue of the traditional intelligent diagnostic algorithm\u2019s insufficient utilization of correlation characteristics within the time series of fault signals and to meet the challenges of accuracy and computational complexity in rotating machinery fault diagnosis, a novel approach based on a recurrence binary plot (RBP) and a lightweight, deep, separable, dilated convolutional neural network (DSD-CNN) is proposed. Firstly, a recursive encoding method is used to convert the fault vibration signals of rotating machinery into two-dimensional texture images, extracting feature information from the internal structure of the fault signals as the input for the model. Subsequently, leveraging the excellent feature extraction capabilities of a lightweight convolutional neural network embedded with attention modules, the fault diagnosis of rotating machinery is carried out. The experimental results using different datasets demonstrate that the proposed model achieves excellent diagnostic accuracy and computational efficiency. Additionally, compared with other representative fault diagnosis methods, this model shows better anti-noise performance under different noise test data, and it provides a reliable and efficient reference solution for rotating machinery fault-classification tasks.<\/jats:p>","DOI":"10.3390\/e26080675","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T09:48:54Z","timestamp":1723196934000},"page":"675","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Intelligent Fault Diagnosis Method for Rotating Machinery Based on Recurrence Binary Plot and DSD-CNN"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1094-9769","authenticated-orcid":false,"given":"Yuxin","family":"Shi","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8598-2343","authenticated-orcid":false,"given":"Hongwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2287-9514","authenticated-orcid":false,"given":"Wenlei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3831-9716","authenticated-orcid":false,"given":"Ruoyang","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liang, G., Gumabay, M.V.N., Zhang, Q., and Zhu, G. 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