{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:20:33Z","timestamp":1767705633504,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:00:00Z","timestamp":1661904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"],"award-info":[{"award-number":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"],"award-info":[{"award-number":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Program Key Project of Shaanxi Province","award":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"],"award-info":[{"award-number":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"]}]},{"name":"Natural Science Special Project of Education Department of Shaanxi Provincial Government","award":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"],"award-info":[{"award-number":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"]}]},{"name":"Doctoral Dissertation Innovation Fund of Xi\u2019an University of Technology","award":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"],"award-info":[{"award-number":["2019YFB1707200","52075435","2022JZ-30","21JK0805","252072105"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the continuous development of artificial intelligence, data-driven fault diagnosis methods are gradually attracting widespread attention. However, in practical industrial applications, noise in the working environment is inevitable. This leads to the fact that the performance of traditional intelligent diagnosis methods is hardly sufficient to satisfy the requirements. In this paper, a developed intelligent diagnosis framework is proposed to overcome this deficiency. The main contributions of this paper are as follows: Firstly, a fault diagnosis model is established using EfficientNet, which achieves optimal diagnosis performance with limited computing resources. Secondly, an attention mechanism is introduced into the basic model for accurately establishing the relationship between fault features and fault modes, while improving the diagnosis accuracy in complex noise environments. Finally, to explain the proposed method, the weights and features of the model are visualized, and further attempts are made to analyze the reasons for the high performance of the model. The comprehensive experiment results reveal the superiority of the proposed method in terms of accuracy and stability in comparison with other benchmark diagnosis approaches. The diagnostic accuracy under actual working conditions is 86.24%.<\/jats:p>","DOI":"10.3390\/s22176570","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T03:55:38Z","timestamp":1662004538000},"page":"6570","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["An Attention EfficientNet-Based Strategy for Bearing Fault Diagnosis under Strong Noise"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1754-3736","authenticated-orcid":false,"given":"Bingbing","family":"Hu","sequence":"first","affiliation":[{"name":"Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4933-527X","authenticated-orcid":false,"given":"Jiahui","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Jimei","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Jiajuan","family":"Qing","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1177\/0954408920971976","article-title":"Fault diagnosis of various rotating equipment using machine learning approaches\u2014a review","volume":"235","author":"Manikandan","year":"2021","journal-title":"Proc. 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