{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T22:02:48Z","timestamp":1767909768853,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Heilongjiang Natural Science Foundation Project","award":["LH2022E116"],"award-info":[{"award-number":["LH2022E116"]}]},{"name":"Heilongjiang Natural Science Foundation Project","award":["145209402"],"award-info":[{"award-number":["145209402"]}]},{"name":"Basic scientific research operating expenses project of Heilongjiang Province","award":["LH2022E116"],"award-info":[{"award-number":["LH2022E116"]}]},{"name":"Basic scientific research operating expenses project of Heilongjiang Province","award":["145209402"],"award-info":[{"award-number":["145209402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time\u2013frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy.<\/jats:p>","DOI":"10.3390\/s24165285","type":"journal-article","created":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T09:29:41Z","timestamp":1723714181000},"page":"5285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA"],"prefix":"10.3390","volume":"24","author":[{"given":"Qingming","family":"Hu","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161006, China"},{"name":"The Engineering Technology Research Center for Precision Manufacturing Equipment and Industrial Perception of Heilongjiang Province, Qiqihar University, Qiqihar 161006, China"},{"name":"The Collaborative Innovation Center for Intelligent Manufacturing Equipment Industrialization, Qiqihar University, Qiqihar 161006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinjie","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanqi","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingtao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.isatra.2022.06.047","article-title":"Bearing multi-fault diagnosis with iterative generalized demodulation guided by enhanced rotational frequency matching under time-varying speed conditions","volume":"133","author":"Zhao","year":"2023","journal-title":"ISA Trans."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11315","DOI":"10.1007\/s11071-023-08405-x","article-title":"A novel hierarchical transferable network for rolling bearing fault diagnosis under variable working conditions","volume":"111","author":"Weng","year":"2023","journal-title":"Nonlinear Dyn."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method","volume":"65","author":"Wen","year":"2018","journal-title":"IEEE Trans. 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