基于相似性度量迁移学习的轴承故障诊断

PDF(2212 KB)

振动与冲击 ›› 2022, Vol. 41 ›› Issue (16) : 217-223.

论文

基于相似性度量迁移学习的轴承故障诊断

徐易芸1，马健1，陈良1，沈长青2，李奇1，孔林3

作者信息 +

A bearing fault diagnosis based on similarity measurement for transfer learning

XU Yiyun1,MA Jian1,CHEN Liang1,SHEN Changqing2,LI Qi1,KONG Lin3

Author information +

文章历史 +

摘要

滚动轴承工况多变，受负荷、转速等因素影响，故障信号的特征分布偏移会显著降低故障诊断模型的泛化能力。针对此问题，提出一种基于相似性度量迁移学习的轴承故障诊断方法。将迁移学习和相似性度量的思想结合，通过相关对齐损失计算变工况故障特征之间的相关性，最小化源域和目标域特征之间的分布差异。同时最大化输入特征与中心特征的相似性，利用目标域预测标签中包含的故障分类信息，提高故障特征聚类的准确性，来增加高相关性特征对模型的贡献度，减小非相关特征的影响。最后利用学习到的特征实现故障分类。在CWRU和自搭建实验平台上进行了对比实验，证明了所述方法能够更加准确地分类故障信号，更好解决不同工况下由特征分布偏移带来的故障诊断难点问题。
关键词：滚动轴承；故障诊断；迁移学习；相似性度量；特征分布

Abstract

According to the variable working conditions of rolling bearings due to the influence of load, speed and other factors, the feature distribution deviation of fault signals will significantly reduce the generalization ability of fault diagnosis model. To solve this problem, a bearing fault diagnosis method based on similarity measurement for transfer learning was proposed. The idea of transfer learning and similarity measurement were combined. The correlation between fault features under variable conditions is calculated through correlation alignment loss, and the distribution difference between source domain and target domain features is minimized. At the same time, the similarity between the input feature and the central feature is maximized, and the fault classification information contained in the label predicted by the target domain is used to improve the accuracy of fault feature clustering, so as to increase the contribution of highly correlated features to the model and reduce the influence of non-correlated features. Finally, the features learned are used to implement fault classification. Comparison experiments on CWRU and self-built experimental platform prove that the proposed method can classify fault signals more accurately and solve the difficult problems of fault diagnosis caused by feature distribution deviation under different working conditions.
Key words: rolling bearing; fault diagnosis; transfer learning; similarity measurement; feature distribution

导出引用

徐易芸1，马健1，陈良1，沈长青2，李奇1，孔林3. 基于相似性度量迁移学习的轴承故障诊断[J]. 振动与冲击, 2022, 41(16): 217-223

XU Yiyun1,MA Jian1,CHEN Liang1,SHEN Changqing2,LI Qi1,KONG Lin3. A bearing fault diagnosis based on similarity measurement for transfer learning[J]. Journal of Vibration and Shock, 2022, 41(16): 217-223

参考文献

[1] Ciabattoni L, Ferracuti F, Freddi A, et al. Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines [J]. IEEE Transactions on Industrial Electronics, 2018,65(5):4301-4310.
[2] 雷亚国, 何正嘉. 混合智能故障诊断与预示技术的应用进展[J]. 振动与冲击, 2011, 30(9):129-135.
LEI Yaguo, HE Zhengjia. Advances in applications of hybrid intelligent fault diagnosis and prognosis technique[J]. Journal of Vibration and Shock, 2011, 30(9):129-135.
[3] He Q. Time-frequency manifold for nonlinear feature extraction in machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2013, 35(1-2):200-218.
[4] Peng Z K, Chu F L, Tse P W. Detection of the rubbing-caused impacts for rotor-stator fault diagnosis using reassigned scalogram[J]. Mechanical Systems and Signal Processing, 2005, 19(2):391-409.
[5] Yan R, Gao R X, Chen X. Wavelets for fault diagnosis of rotary machines: A review with applications[J]. Signal Processing, 2014, 96(PART A):1-15.
[6] Kankar P K, Sharma S C, Harsha S P. Rolling element bearing fault diagnosis using wavelet transform[J]. Neurocomputing, 2011, 74(10):1638-1645.
[7] Janssens O, Slavkovikj V, Vervisch B, et al. Convolutional Neural Network Based Fault Detection for Rotating Machinery[J]. Journal of Sound and Vibration, 2016, 377:331-345.
[8] Li Y, Chen P, Wang K, et al. A deep convolutional autoencoder for wind turbine blades health condition monitoring based on FBG strain gauges measurement[C]// Proceedings of the 7th Asia-Pacific Workshop on Structural Health Monitoring, 2018: 973-981.
[9] Chen Z, Li W. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7):1693-1702.
[10] Abed W, Sharma S, Sutton R, et al. Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions[J]. Journal of Control, Automation and Electrical Systems, 2015, 26(3):241-254.
[11] Liu H, Zhou J, Xu Y, et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks[J]. Neurocomputing, 2018, 315:412-424.
[12] 王应晨, 段修生. 深度学习融合模型在机械故障诊断中的应用[J]. 振动、测试与诊断, 2019, 39(6):1271-1276.
WANG Yingchen, DUAN Xiusheng. Improved Deep Learning Fusion Model in Fault Diagnosis[J]. Journal of Vibration, Measurement & Diagnosis, 2019, 39(6):1271-1276.
[13] 洪骥宇, 王华伟, 车畅畅, 等. 改进降噪自编码的航空发动机气路故障诊断[J].振动、测试与诊断, 2019, 39(3):603-610.
HONG Jiyu, WANG Huawei, CHE Changchang, et al. Improved Deep Learning Fusion Model in Fault Diagnosis[J]. Journal of Vibration, Measurement & Diagnosis, 2019, 39(3):603-610.
[14] Lu W, Liang B, Cheng Y, et al. Deep Model Based Domain Adaptation for Fault Diagnosis[J] IEEE Transactions on Industrial Electronics, 2017, 64(3):2296-2305.
[15] Guo L, Lei Y, Xing S, et al. Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines with Unlabeled Data[J] IEEE Transactions on Industrial Electronics, 2019, 66(9):7316-7325.
[16] Wen L, Gao L, Li X. A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J] IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(1):136-144.
[17] Wang X, He H, Li L. A hierarchical deep domain adaptation approach for fault diagnosis of power plant thermal system[J]. IEEE Transactions on Industrial Informatics, 2019, 15(9): 5139-5148.
[18] 雷亚国, 杨彬, 杜兆钧, 等. 大数据下机械装备故障的深度迁移诊断方法[J]. 机械工程学报, 2019, 55(7):1-8.
LEI Yaguo, YANG Bin, DU Zhaojun, et al. Deep Transfer Diagnosis Method for Machinery in Big Data Era[J]. Journal of Mechanical Engineering, 2019, 55(7):1-8.
[19] An Z, Li S, Wang J, et al. Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method[J]. Neurocomputing, 2019, 352:42–53.
[20] Kingma D P, Ba J L. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980, 2014.
[21] Smith W A, Randall R B. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64-65:100-131.
[22] Pan S J, Tsang I W, Kwok J T, et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 2011, 22(2):199–210.
[23] Long M, Wang J, Ding G, et al. Transfer feature learning with joint distribution adaptation[J] IEEE International Conference on Computer Vision, 2013:2200–2207.