融合时频特征的多源无监督域自适应轴承故障诊断方法

PDF(5236 KB)

振动与冲击 ›› 2024, Vol. 43 ›› Issue (13) : 12-24.

论文

金怀平1,2,刘志泳1,2,王彬1,2,钱斌1,2,刘海鹏1,2

作者信息 +

A multi-source unsupervised domain adaptive bearing fault diagnosis method integrating time-frequency features

JIN Huaiping1,2, LIU Zhiyong1,2, WANG Bin1,2, QIAN Bin1,2, LIU Haipeng1,2

Author information +

文章历史 +

摘要

无监督域自适应已成为多工况下轴承故障诊断的一种重要方法。然而，现有多源无监督域自适应方法往往忽略不同视角信号对于跨域故障诊断的贡献，不足以全面表达轴承的故障特征。此外，这些方法的不同源域对同一目标域的预测结果存在差异。为此，提出一种融合时频特征的多源无监督域自适应(Time-Frequency Features based Multi-source Unsupervised Domain Adaptation, TFFMUDA)轴承故障诊断方法。该方法以时域和频域信号为输入，通过特征耦合机制实现两种故障特征的互补，并利用分类器对齐策略增强了不同源域对于同一目标域的诊断一致性。通过实际轴承故障案例的实验结果表明，所提方法相较于现有无监督域自适应轴承故障诊断方法能获得更清晰的故障类决策边界并具有更好的目标域诊断精度。

Abstract

Unsupervised domain adaptation methods have become an important approach for bearing fault diagnosis under multiple operating conditions. However, existing multi-source unsupervised domain adaptation methods often ignore the contribution of signals from different perspectives to cross-domain fault diagnosis, thus failing to comprehensively represent the fault characteristics of bearings. Additionally, these methods often encounter discrepancies of the prediction results from different source domains for the same target domain task. To address these issues, a time-frequency features fused multi-source unsupervised domain adaptation (TFFMUDA) method is proposed for bearing fault diagnosis. TFFMUDA takes both time-domain and frequency-domain signals as inputs, which interact through a feature coupling mechanism. Meanwhile, the diagnostic consistency of different source domains for the same target domain is guaranteed through classifier alignment strategy. Experimental results on a real bearing fault case demonstrate that the proposed method achieves clearer decision boundaries for fault classes and exhibits improved accuracy for bearing fault diagnosis compared to existing domain adaptation methods.

导出引用

金怀平1,2,刘志泳1,2,王彬1,2,钱斌1,2,刘海鹏1,2. 融合时频特征的多源无监督域自适应轴承故障诊断方法[J]. 振动与冲击, 2024, 43(13): 12-24

JIN Huaiping1,2, LIU Zhiyong1,2, WANG Bin1,2, QIAN Bin1,2, LIU Haipeng1,2. A multi-source unsupervised domain adaptive bearing fault diagnosis method integrating time-frequency features[J]. Journal of Vibration and Shock, 2024, 43(13): 12-24

参考文献

[1] Cao H, Shao H, Zhong X, et al. Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds[J]. Journal of Manufacturing Systems, 2022, 62: 186-198. [2] Chen X, Zhang B, Gao D. Bearing fault diagnosis base on multi-scale CNN and LSTM model[J]. Journal of Intelligent Manufacturing, 2021, 32: 971-987. [3] M. E. H. Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 984–993, 2000. [4] P. Wen, X. Li, N. Hou, and S. Mu, “Distributed recursive fault estimation with binary encoding schemes over sensor networks,” Systems Science & Control Engineering, vol. 10, no. 1, pp. 417–427, 2022 [5] Zhao X, Jia M, Lin M. Deep Laplacian auto-encoder and its application into imbalanced fault diagnosis of rotating machinery. Measurement 2020;152:107320. [6] Pan T, Chen J, Xie J, Chang Y, Zhou Z. Intelligent fault identification for industrial automation system via multi-scale convolutional generative adversarial network with partially labeled samples. ISA Trans 2020;101:379–89. [7] Wu C, Jiang P, Ding C, Feng F, Chen T. Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Comput Ind 2019;108:53–61. [8] Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 2020, 109(1): 43-76. [9] Han T, Liu C, Yang W, Jiang D. Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application. ISA Trans 2020;97:269–81. [10] Qian W, Li S, Jiang X. Deep transfer network for rotating machine fault analysis. Pattern Recognit 2019;96:106993. [11] Ma P, Zhang H, Fan W, Wang C. A diagnosis framework based on domain adaptation for bearing fault diagnosis across diverse domains. ISA Trans 2020;99:465–78. [12] Y. Mansour, M. Mohri, A. Rostamizadeh, Domain adaptation with multiple sources, in: Adv. Neural Inf. Process. Syst, 2009, pp. 1041–1048. [13] Zhu J, Chen N, Shen C, et al. Multi-source Unsupervised Domain Adaptation for Machinery Fault Diagnosis under Different Working Conditions[C]//2020 IEEE 18th International Conference on Industrial Informatics (INDIN). IEEE, 2020, 1: 755-762. [14] Tian J, Han D, Li M, et al. A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis[J]. Knowledge-Based Systems, 2022, 243: 108466. [15] Chai Z, Zhao C. Deep transfer learning based multisource adaptation fault diagnosis network for industrial processes[J]. IFAC-PapersOnLine, 2021, 54(3): 49-54. [16] Li X, Jiang H, Xie M, et al. A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with multi-source domains[J]. Advanced Engineering Informatics, 2022, 51: 101480. [17] Yang B, Xu S, Lei Y, et al. Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults[J]. Mechanical Systems and Signal Processing, 2022, 162: 108095. [18] Chai Z, Zhao C, Huang B. Multisource-refined transfer network for industrial fault diagnosis under domain and category inconsistencies[J]. IEEE Transactions on Cybernetics, 2021, 52(9): 9784-9796. [19] 钱思宇，秦东晨，陈江义，袁峰. 基于卷积神经网络的领域适配模型的多工况迁移的轴承故障诊断[J]. 振动与冲击, 2022, 41(24): 192-200. QIAN Siyu,QIN Dongchen,CHEN Jiangyi,YUAN Feng. Bearing fault diagnosis based on a domain adaptation model of convolutional neural network under multiple working conditions. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(24): 192-200. [20] Yan H, Ding Y, Li P, et al. Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2272-2281. [21] Gretton A, Sejdinovic D, Strathmann H, et al. Optimal kernel choice for large-scale two-sample tests[J]. Advances in neural information processing systems, 2012, 25. [22] Liu X, Wang J, Meng S, et al. Multi-view rotating machinery fault diagnosis with adaptive co-attention fusion network[J]. Engineering Applications of Artificial Intelligence, 2023, 122: 106138. [23] Lessmeier C, Kimotho J K, Zimmer D, et al. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification[C]//PHM Society European Conference. 2016, 3(1). [24] Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation[C]//International conference on machine learning. PMLR, 2015: 1180-1189. [25] Sun B, Saenko K. Deep coral: Correlation alignment for deep domain adaptation[C]//Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14. Springer International Publishing, 2016: 443-450. [26] Qian Q, Qin Y, Luo J, et al. Deep discriminative transfer learning network for cross-machine fault diagnosis[J]. Mechanical Systems and Signal Processing, 2023, 186: 109884. [27] Zhu Y, Zhuang F, Wang D. Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources[C]//Proceedings of the AAAI conference on artificial intelligence. 2019, 33(01): 5989-5996. [28] Tian J, Han D, Li M, et al. A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis[J]. Knowledge-Based Systems, 2022, 243: 108466.