{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:15:31Z","timestamp":1780384531762,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology of China","award":["G2021171024L"],"award-info":[{"award-number":["G2021171024L"]}]},{"name":"Ministry of Science and Technology of China","award":["S2022-ZC-GXYZ-0015"],"award-info":[{"award-number":["S2022-ZC-GXYZ-0015"]}]},{"name":"International Innovation Research Base of Shaanxi Province","award":["G2021171024L"],"award-info":[{"award-number":["G2021171024L"]}]},{"name":"International Innovation Research Base of Shaanxi Province","award":["S2022-ZC-GXYZ-0015"],"award-info":[{"award-number":["S2022-ZC-GXYZ-0015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The reliable and safe operation of industrial systems needs to detect and diagnose bearing faults as early as possible. Intelligent fault diagnostic systems that use deep learning convolutional neural network (CNN) techniques have achieved a great deal of success in recent years. In a traditional CNN, the fully connected layer is located in the final three layers, and such a layer consists of multiple layers that are all connected. However, the fully connected layer of the CNN has the disadvantage of too many training parameters, which makes the model training and testing time longer and incurs overfitting. Additionally, because the working load is constantly changing and noise from the place of operation is unavoidable, the efficiency of intelligent fault diagnosis techniques suffers great reductions. In this research, we propose a novel technique that can effectively solve the problem of traditional CNN and accurately identify the bearing fault. Firstly, the best pre-trained CNN model is identified by considering the classification\u2019s success rate for bearing fault diagnosis. Secondly, the selected CNN model is modified to effectively reduce the parameter quantities, overfitting, and calculating time of this model. Finally, the best classifier is identified to make a hybrid model concept to achieve the best performance. It is found that the proposed technique performs well under different load conditions, even in noisy environments, with variable signal-to-noise ratio (SNR) values. Our experimental results confirm that this proposed method is highly reliable and efficient in detecting and classifying bearing faults.<\/jats:p>","DOI":"10.3390\/s23187764","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T08:01:30Z","timestamp":1694160090000},"page":"7764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8921-001X","authenticated-orcid":false,"given":"Mohammad","family":"Mohiuddin","sequence":"first","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong 4349, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Saiful","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong 4349, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirajul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong 4349, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Sipon","family":"Miah","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technology, Islamic University, Kushtia 7003, Bangladesh"},{"name":"Department of Signal Theory and Communications, University Carlos III of Madrid (UC3M), 28911 Madrid, Spain"},{"name":"IVR Low-Carbon Research Institute, School of Energy and Electrical Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7514-3239","authenticated-orcid":false,"given":"Ming-Bo","family":"Niu","sequence":"additional","affiliation":[{"name":"IVR Low-Carbon Research Institute, School of Energy and Electrical Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S0003-682X(97)00018-2","article-title":"Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition","volume":"53","author":"Heng","year":"1998","journal-title":"Appl. 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