{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T02:24:54Z","timestamp":1778898294020,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"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":["51805151"],"award-info":[{"award-number":["51805151"]}],"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":["21B460004"],"award-info":[{"award-number":["21B460004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Scientific Research Project of the University of Henan Province of China","award":["51805151"],"award-info":[{"award-number":["51805151"]}]},{"name":"Key Scientific Research Project of the University of Henan Province of China","award":["21B460004"],"award-info":[{"award-number":["21B460004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Existing diagnosis methods for bearing faults often neglect the temporal correlation of signals, resulting in easy loss of crucial information. Moreover, these methods struggle to adapt to complex working conditions for bearing fault feature extraction. To address these issues, this paper proposes an intelligent diagnosis method for compound faults in metro traction motor bearings. This method combines multisignal fusion, Markov transition field (MTF), and an optimized deep residual network (ResNet) to enhance the accuracy and effectiveness of diagnosis in the presence of complex working conditions. At the outset, the acquired vibration and acoustic emission signals are encoded into two-dimensional color feature images with temporal relevance by Markov transition field. Subsequently, the image features are extracted and fused into a set of comprehensive feature images with the aid of the image fusion framework based on a convolutional neural network (IFCNN). Afterwards, samples representing different fault types are presented as inputs to the optimized ResNet model during the training phase. Through this process, the model\u2019s ability to achieve intelligent diagnosis of compound faults in variable working conditions is realized. The results of the experimental analysis verify that the proposed method can effectively extract comprehensive fault features while working in complex conditions, enhancing the efficiency of the detection process and achieving a high accuracy rate for the diagnosis of compound faults.<\/jats:p>","DOI":"10.3390\/s23146281","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T01:58:14Z","timestamp":1689040694000},"page":"6281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Intelligent Diagnosis of Rolling Bearings Fault Based on Multisignal Fusion and MTF-ResNet"],"prefix":"10.3390","volume":"23","author":[{"given":"Kecheng","family":"He","sequence":"first","affiliation":[{"name":"School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China"},{"name":"Intelligent Numerical Control Equipment Engineering Laboratory of Henan Province, Luoyang 471003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanwei","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China"},{"name":"Intelligent Numerical Control Equipment Engineering Laboratory of Henan Province, Luoyang 471003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China"},{"name":"Intelligent Numerical Control Equipment Engineering Laboratory of Henan Province, Luoyang 471003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tancheng","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China"},{"name":"Intelligent Numerical Control Equipment Engineering Laboratory of Henan Province, Luoyang 471003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.renene.2019.06.094","article-title":"Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method","volume":"146","author":"Liu","year":"2020","journal-title":"Renew. 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