{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T21:27:31Z","timestamp":1772573251763,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:00:00Z","timestamp":1698192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51675350"],"award-info":[{"award-number":["51675350"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021JH1\/10400031"],"award-info":[{"award-number":["2021JH1\/10400031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201cJie Bang Gua Shuai\u201d Key Technologies R&amp;D Program of Liaoning Province","award":["51675350"],"award-info":[{"award-number":["51675350"]}]},{"name":"\u201cJie Bang Gua Shuai\u201d Key Technologies R&amp;D Program of Liaoning Province","award":["2021JH1\/10400031"],"award-info":[{"award-number":["2021JH1\/10400031"]}]},{"name":"Liaoning Province Research Center for Wastewater Treatment and Reuse","award":["51675350"],"award-info":[{"award-number":["51675350"]}]},{"name":"Liaoning Province Research Center for Wastewater Treatment and Reuse","award":["2021JH1\/10400031"],"award-info":[{"award-number":["2021JH1\/10400031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The method of acoustic radiation signal detection not only enables contactless measurement but also provides comprehensive state information during equipment operation. This paper proposes an enhanced feature extraction network (EFEN) for fault diagnosis of rolling bearings based on acoustic signal feature learning. The EFEN network comprises four main components: the data preprocessing module, the information feature selection module (IFSM), the channel attention mechanism module (CAMM), and the convolutional neural network module (CNNM). Firstly, the one-dimensional acoustic signal is transformed into a two-dimensional grayscale image. Then, IFSM utilizes three different-sized convolution filters to process input image data and fuse and assign weights to feature information that can attenuate noise while highlighting effective fault information. Next, a channel attention mechanism module is introduced to assign weights to each channel. Finally, the convolutional neural network (CNN) fault diagnosis module is employed for accurate classification of rolling bearing faults. Experimental results demonstrate that the EFEN network achieves high accuracy in fault diagnosis and effectively detects rolling bearing faults based on acoustic signals. The proposed method achieves an accuracy of 98.52%, surpassing other methods in terms of performance. In comparative analysis of antinoise experiments, the average accuracy remains remarkably high at 96.62%, accompanied by a significantly reduced average iteration time of only 0.25 s. Furthermore, comparative analysis confirms that the proposed algorithm exhibits excellent accuracy and resistance against noise.<\/jats:p>","DOI":"10.3390\/s23218703","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T06:19:58Z","timestamp":1698214798000},"page":"8703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Enhanced Feature Extraction Network Based on Acoustic Signal Feature Learning for Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"23","author":[{"given":"Yuanqing","family":"Luo","sequence":"first","affiliation":[{"name":"School of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, China"}]},{"given":"Wenxia","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, China"}]},{"given":"Shuang","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Control Engineering, Baicheng Normal University, Baicheng 137000, China"}]},{"given":"Xueyong","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang 110870, China"}]},{"given":"Xiaoqi","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China"}]},{"given":"Feng","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8100","DOI":"10.1109\/JSEN.2021.3049277","article-title":"A Group Decision Optimization Analogy-Based Deep Learning Architecture for Multiclass Pathology Classification in a Voice Signal","volume":"21","author":"Wahengbam","year":"2021","journal-title":"IEEE Sens. 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