文献类型：期刊文献

英文题名：Rolling bearing fault diagnosis method using time-frequency information integration and multi-scale TransFusion network

作者：Wang, Zekun[1,2];Xu, Zifei[3,4,5];Cai, Chang[2];Wang, Xiaodong[1];Xu, Jianzhong[1,2];Shi, Kezhong[2];Zhong, Xiaohui[2];Liao, Zhiqiang[6];Li, Qing 'an[2]

机构：[1]North China Elect Power Univ, Sch Energy Power & Mech Engn, Beijing 102206, Peoples R China;[2]Chinese Acad Sci, Inst Engn Thermophys, Beijing 100190, Peoples R China;[3]Liverpool John Moores Univ, Liverpool Logist Offshore & Marine LOOM Res Inst, Liverpool L3 3AF, England;[4]Liverpool John Moores Univ, Mech Engn & Mat Res Ctr MEMARC, Liverpool L3 3AF, England;[5]Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China;[6]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524088, Peoples R China

年份：2024

卷号：284

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:001156312500001)、、EI(收录号：20240115323872)、Scopus(收录号：2-s2.0-85181168145)、WOS

基金：This work is supported by the National Key R & D Program of China (No. 2022YFE0207000) , the National Nature Science Foundation of China (No. 52206283) , the Marie Sklodowska-Curie Postdoctoral Fellowship (No. EPSRC EP/Y014235/1) , the High-tech industrialization special fund project of scientific and technological cooperation between Jilin Province and Chinese Academy of Sciences (No. 2023SYHZ0001) , and the State Key Laboratory of Mechanical System and Vibration (Grant, No. MSV202411) .

语种：英文

外文关键词：Deep Learning; Fault diagnosis; Rolling bearings; Time-frequency symmetry dot pattern; Transformer neural network

外文摘要：Advances in deep learning methods have demonstrated remarkable development in diagnosing faults of rotating machinery. The currently popular deep neural networks suffer from design flaws in their network structure, leading to issues of long-term dependencies in fault diagnosis models built upon conventional deep neural networks. Consequently, such models exhibit insufficient global perceptual capabilities towards fault features. Furthermore, how accurately pre-trained models can diagnose faults is hugely impacted by changes in bearings' working conditions. To tackle the aforementioned issues, this study puts forth a multi-scale TransFusion (MSTF) model for diagnosing faults in rolling bearings under multiple operating conditions. Firstly, a time-frequency symmetric dot pattern transformation technique is designed to transform the original vibration signals into two-dimensional representations. This method can effectively highlight the distinctions between different fault types. Secondly, a multi-scale feature fusion module is established, which fully extracts low-level features from the time-frequency signals and reduces the complexity of the subsequent attention calculations. Meanwhile, relying on the advantages of the Transformer model in capturing global dependencies, the long-range periodic fault information is deeply mined. Finally, multi-head and multi-layer attention are visualized to enhance the interpretability of the model. After analyzing two case studies with both public and experimental datasets, the examination demonstrated that the developed model outperformed other state-of-the-art models. The diagnostic model developed in this study exhibits the ability to accurately diagnose bearing faults across multiple operating conditions while maintaining high robustness to signals contaminated with noise.