Fault diagnosis of rolling bearing under complex operating conditions based on deep residual shrinkage transfer network

PDF(1415 KB)

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (3) : 194-200.

CHEN Renxiang1, ZHANG Xiao1, ZHU Yuqing1, XU Xiangyang1, YANG Baojun2

Author information +

History +

Abstract

Aiming the diagnosis of rolling bearing faults in complex operating conditions with strong noise and different speeds. Rolling bearing fault diagnosis method under complex working conditions based on deep residual shrinkage transfer network (DRSTN) was proposed. The domain adaptation layer is added to the deep residual shrinkage network to construct the deep residual shrinkage migration network with noise reduction and adaptation ability, so as to reduce the interference caused by noise and the distribution difference caused by speed change. Firstly, a set of thresholds are automatically set by using the attention mechanism to learn the importance of each feature channel after convolution layer, and the features in the threshold range are set to zero by soft threshold to reduce the interference caused by noise. Then, the feature distribution of the two domains is aligned by edge distribution to reduce the distribution difference caused by speed change. Finally, the fault diagnosis of rolling bearing under end-to-end complex conditions is realized under Softmax classification layer. The experimental results of rolling bearing fault diagnosis under complex conditions verify the feasibility and effectiveness of the proposed method.

Key words

noise interference / different speed / residual shrinkage transfer / rolling bearing / fault diagnosis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

CHEN Renxiang1, ZHANG Xiao1, ZHU Yuqing1, XU Xiangyang1, YANG Baojun2. Fault diagnosis of rolling bearing under complex operating conditions based on deep residual shrinkage transfer network[J]. Journal of Vibration and Shock, 2024, 43(3): 194-200

References

[1] 田晶,王英杰,王志,等.基于EEMD与空域相关降噪的滚动轴承故障诊断方法[J].仪器仪表学报,2018,39(07):144-151. TIAN Jin, WANG Ying-jie, WANG Zhi, et al. Fault diagnosis for rolling bearing based on EEMD and spatial correlation denoising[J], Chinese Journal of Scientific Instrument,2018,39(07):144-151. [2] 侯文擎,叶鸣,李巍华.基于改进堆叠降噪自编码的滚动轴承故障分类[J].机械工程学报,2018,54(07):87-96. HOU Wwen-qing, LI Ming, LI Wei-hua. Rolling Element Bearing Fault Classification Using Improved Stacked De-noising Auto-encoders [J]. Mechanical Systems and Signal Processing, 2018,54(07):87-96. [3] 赵小强,张亚洲.改进CNN的滚动轴承变工况故障诊断[J].西安交通大学学报,2021(12):1-11. ZHAO Xiao-qiang, ZHANG Ya-zhou. Improved CNN Fault Diagnosis Method of Rolling Bearings under Variable Working Conditions[J]. Journal of Xi'an Jiaotong University, 2021(12):1-11. [4] 袁壮,董瑞,张来斌,等.深度领域自适应及其在跨工况故障诊断中的应用[J].振动与冲击,2020,39(12):281-288. YUAN Zhuang, DONG Rui, ZHANG Lai-bin, et al. Deep domain adaptation and its application in fault diagnosis across working conditions[J], Journal of Vibration and Shock, 2020,39(12):281-288. [5] 陈仁祥,唐林林,孙健,等.一维深度子领域适配的不同转速下旋转机械复合故障诊断[J].仪器仪表学报,2021,42(05):227-234. CHEN Ren-xiang, TANG Lin-lin, SUN Jian, et al. Composite Fault Diagnosis of Rotating Machinery under Different Speed Based on one Dimensional Deep Subdomain Adaption[J]. Chinese Journal of Scientific Instrument, 2021,42(05):227-234. [6] WANG X, MAO D X, LI X D. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network[J]. Measurement, 2021, 173(6):108518. [7] ZHAO M, ZHONG S, FU X, et al. Deep Residual Shrinkage Networks for Fault Diagnosis[J]. IEEE Transactions on Industrial Informatics, 2020，16(7)：4681-4690. [8] ZOU Y, LIUY, DENG J, et al. A novel transfer learning method for bearing fault diagnosis under different working conditions[J]. Measurement, 2021, 171:108767. [9] 康守强, 胡明武, 王玉静, 等.基于特征迁移学习的变工况下滚动轴承故障诊断方法[J].中国电机工程学报,2019,39(03):764-772. KANG Shou-qiang, HU Ming-wu, WANG Yu-jing, et al. Fault diagnosis method of rolling bearing under variable conditions based on feature transfer learning [J]. China Journal of Electrical Engineering, 2019,39(03):764-772. [10] Li F, Tang T, Tang B, et al. Deep Convolution Domain-adversarial Transfer Learning for Fault Diagnosis of Rolling Bearings[J]. Measurement, 2020, 169(5):108339. [11] Ioffe S, Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift[J]. 2015, pp. 448–456. [12] BORGWARDT K M, GRETTON A, RASCH M J, et al. Integrating Structured Biological Data by Kernel Maximum Mean Discrepancy[J]. Bioinformatics,2006, 22(14): 49-57. [13] CHEN C, SHEN F, YAN R. Topic Correlation Analysis for Bearing Fault Diagnosis Under Variable OperatingConditions[J]. Journal of Physics Conference Series, 2017, 842:1-9. [14] LONG M, CAO Y, WANG J, et al. Learning transferable features with deep adaptation networks[J]. Proceedings of the 32nd International Conference on International Conference on Machine Learning, 2015, 37(7): 97–105. [15] 郭亮，董勋，高宏力，等．无标签数据下基于特征知识迁移的机械设备智能故障诊断［J］．仪器仪表学报，2019，40(8): 58-64． GUO Liang，DONG Xun，GAO Hong-li，et al． Feature knowledge transfer based intelligent fault diagnosis method of machines with unlabeled data［J］． Chinese Journal of Scientific Instrument，2019，40(8): 58-64.