{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:52:13Z","timestamp":1780674733136,"version":"3.54.1"},"reference-count":39,"publisher":"World Scientific Pub Co Pte Ltd","issue":"18","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42375146"],"award-info":[{"award-number":["42375146"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Laboratory for Advanced Computing and Intelligence Engineering, and Laoshan Laboratory","award":["LSKJ202202100"],"award-info":[{"award-number":["LSKJ202202100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:p> With the rapid development of intelligent manufacturing technology, the structure of industrial equipment has become more sophisticated, resulting in frequent equipment failures. However, traditional anomaly diagnosis methods suffer the issues of insufficient accuracy and are always unable to identify anomalies in time. To solve the above problems, a deep learning-based equipment anomaly diagnosis method in industrial production scenarios is proposed in this paper. Specifically, a combination model based on Selective Kernel (SK) convolution blocks is designed to improve the accuracy of anomaly identification; a lightweight model based on Depthwise Separable Convolution (DSC) and Attention Mechanism (AM) is proposed to improve the timeliness of anomaly identification; an anomaly analysis and intelligent diagnosis framework is established and implemented to automatically complete anomaly identification tasks for different equipment and practical problems. Finally, we conduct extensive experiments to validate the effectiveness of our methods. The experimental results show that the accuracy of our anomaly identification method is as high as 99.07[Formula: see text], and the calculation amount of the lightweight model is reduced by 84.95[Formula: see text] compared to the baseline model. <\/jats:p>","DOI":"10.1142\/s0218126625500136","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T09:19:06Z","timestamp":1721899146000},"source":"Crossref","is-referenced-by-count":1,"title":["An Equipment Anomaly Diagnosis Method Based on Deep Learning"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3408-9834","authenticated-orcid":false,"given":"Mingshu","family":"Ren","sequence":"first","affiliation":[{"name":"School of Economics & Management, Huaibei Normal University, Huaibei, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6410-2274","authenticated-orcid":false,"given":"Qiong","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Data Science & Engineering, East China Normal University, Shanghai, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1116-1491","authenticated-orcid":false,"given":"Chengyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Basic Product Center, Ucloud Information Technology Limited, Shanghai, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6393-4295","authenticated-orcid":false,"given":"Yao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Data Science & Engineering, MoE Engineering Research Center of Software\/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, P.\u00a0R.\u00a0China"},{"name":"Laboratory for Advanced Computing and Intelligence Engineering, Wuxi, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2024,9,5]]},"reference":[{"key":"S0218126625500136BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2017.03.017"},{"key":"S0218126625500136BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106677"},{"key":"S0218126625500136BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2892667"},{"key":"S0218126625500136BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2019.02.023"},{"key":"S0218126625500136BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2022.09.009"},{"key":"S0218126625500136BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3037082"},{"key":"S0218126625500136BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2023.3297124"},{"key":"S0218126625500136BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109529"},{"key":"S0218126625500136BIB009","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2023.07.035"},{"key":"S0218126625500136BIB010","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2023.03.069"},{"key":"S0218126625500136BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.115585"},{"key":"S0218126625500136BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106622"},{"key":"S0218126625500136BIB013","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2733438"},{"key":"S0218126625500136BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2023.03.096"},{"key":"S0218126625500136BIB015","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105149"},{"key":"S0218126625500136BIB016","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2023.3317296"},{"key":"S0218126625500136BIB017","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107142"},{"key":"S0218126625500136BIB018","first-page":"2465","volume":"36","author":"Liang R.","year":"2021","journal-title":"J. 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