{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T04:16:52Z","timestamp":1775708212505,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T00:00:00Z","timestamp":1710115200000},"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":["11372080"],"award-info":[{"award-number":["11372080"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimodal feature fusion to achieve high-precision fault diagnosis by leveraging the operating state information of bearings in a high-noise environment to the fullest extent possible. First, the horizontal and vertical vibration signals from two sensors are fused using principal component analysis, aiming to provide a more comprehensive description of the bearing\u2019s operating condition, followed by data set segmentation. Following fusion, time-frequency feature maps are generated using a continuous wavelet transform for global time-frequency feature extraction. A first diagnostic model is then developed utilizing a residual neural network. Meanwhile, the feature data is normalized, and 28 time-frequency feature indexes are extracted. Subsequently, a second diagnostic model is constructed using a support vector machine. Lastly, the two diagnosis models are integrated to derive the final model through an ensemble learning algorithm fused at the decision level and complemented by a genetic algorithm solution to improve the diagnosis accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior diagnostic performance with a 97.54% accuracy rate.<\/jats:p>","DOI":"10.3390\/s24061792","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T04:51:12Z","timestamp":1710132672000},"page":"1792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Hybrid Multimodal Feature Fusion with Multi-Sensor for Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"given":"Zhenzhong","family":"Xu","sequence":"first","affiliation":[{"name":"College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Yilin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Foreign Languages, Beijing Institute of Technology, Beijing 102488, China"}]},{"given":"Jiangtao","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","article-title":"Artificial intelligence for fault diagnosis of rotating machinery: A review","volume":"108","author":"Liu","year":"2018","journal-title":"Mech. 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