自监督学习结合对抗迁移的跨工况轴承故障诊断

doi:10.3969/j.issn.1000-1158.2024.09.14

Abstract
Figure/Table
References (26)
Related Citation (9)

Download: PDF (0 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In the application of intelligent bearing fault diagnosis, it is extremely difficult to obtain sufficient real fault data due to the complexity and variability of the actual working conditions, and there exist large differences between the signals in the target and source domains, leading to the problems of difficult feature extraction and classification and weak generalization of the model in the cross-working condition transfer recognition of the deep model. Considering the existence of a large amount of unlabelled data in the target domain, an unsupervised ideas and proposed an improved method based on self-supervised learning combined with adversarial transfer is introduced. Firstly, the pretext tasks are created based on the characteristics of the signal itself to learn from a large amount of unlabeled data and establish the intrinsic connections of fault categories between the source domain and the target domain; then the knowledge learned from the source domain is transferred to the target domain through adversarial domain adaptation and joint maximum mean difference, and finally, it is combined with the pretext task to optimize the difference between the two domains and achieved accurate fault classification in the target domain. Experimental results have verified the feasibility and good performance, accuracy is all higher than 98% in most cases.

Key words： bearing fault diagnosis self-supervised learning cross working conditions adversarial transfer

Received: 15 February 2023 Published: 26 September 2024

PACS:	TB936
	TB973

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WEN Jiangtao
	LIU Zhongyu
	SUN Jiedi
	SHI Peiming

Cite this article:

WEN Jiangtao,LIU Zhongyu,SUN Jiedi, et al. Self-supervised Learning Combined with Adversarial Transfer for Cross-conditions Bearing Fault Diagnosis[J]. Acta Metrologica Sinica, 2024, 45(9): 1360-1369.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.09.14 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I9/1360

［7］	袁彩艳, 孙洁娣, 温江涛, 等. 多域信息融合结合改进残差密集网络的轴承故障诊断［J］.振动与冲击, 2022, 41(4): 200-208.
［2］	LI C, ZHANG S, QIN Y, et al. A systematic review of deep transfer learning for machinery fault diagnosis［J］. Neurocomputing, 2020, 407: 121-135.
［6］	汤亮, 凡焱峰, 徐适斐, 等. 基于贝叶斯优化与改进LeNet-5的滚动轴承故障诊断［J］.计量学报, 2022, 43(7): 913-919.
［9］	唐波, 陈慎慎, 郭必奔, 等. 基于特征参数迁移的滚动轴承故障诊断［J］.计量学报, 2022, 43(3):386-391.
［1］	时培明, 张慧超, 伊思颖, 等. 一种改进的自适应多元变分模态分解轴承故障信号特征提取方法［J］. 计量学报, 2022, 43(10):1326-1334.
［3］	SHAOSHAI H D, JIANG H K, LI X Q, et al. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine［J］. Knowledge-Based Systems, 2018, 140: 1-14.
［8］	温江涛, 张鹏程, 孙洁娣, 等. 残差卷积自编码网络无监督迁移轴承故障诊断［J］. 中国机械工程, 2022, 33(14): 1707-1716.
	TANG B, CHEN S S, GUO B B, et al. Fault Diagnosis of Rolling Bearings Based on Migration of Characteristic Parameters［J］. Acta Metrologica Sinica, 2022, 43(3):386-391.
	SUN J D, LIU B , WEN J T, et al. Fault diagnosis of variable condition bearing based on improved dense network aided by class labels［J］. Journal of Vibration and Shock, 2022, 41(17): 204-212.
［13］	HONG J, YU S C H, CHEN W, et al. Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning［J］. Applied Soft Computing, 2022, 121: 108729.
［15］	WANG H, LIU Z, GE Y, et al. Self-supervised signal representation learning for machinery fault diagnosis under limited annotation data［J］. Knowledge-Based Systems, 2022, 239: 107978.
［17］	XIAO J, ZHANG S, YAO Y, et al. Generative adversarial network with hybrid attention and compromised normalization for multi-scene image conversion［J］. Neural Computing and Applications, 2022, 34(9): 7209-7225.
［4］	YAN X A, LIU Y, JIA M P. Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions［J］. Knowledge-Based Systems, 2020, 193: 105484.
	TANG L, FAN Y F, XU S F, et al. Fault Diagnosis of Rolling Bearing Based on Bayesian Optimization and Improved LeNet-5［J］. Acta Metrologica Sinica, 2022, 43(7): 913-919.
［10］	AN Z H, LI S M, WANG J R, 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.
［12］	LIU X, ZHANG F, HOU Z. Self-supervised Learning: Generative or Contrastive［J］. IEEE Transactions on Knowledge and Data Engineering, 2021, 35(1): 857-876.
［19］	SAN Y Z, DE Q C, DAI H J, et al. Adaptive diagonal total-variation generative adversarial network for super-resolution imaging［J］. IEEE Access, 2020, 8: 57517-57526.
［21］	ZHAO Z, ZHANG Q, YU X, et al. Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study［J］. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 3525828,1-28.
［22］	LI Y WANG X, SI S, et al. Entropy based fault classification using the Case Western Reserve University data: A benchmark study ［J］. IEEE Transactions on Reliability, 2019, 69(2): 754-767.
［25］	LONG M, CAO Z, WANG J, et al. Conditional adversarial domain adaptation［C］//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montreal, Canada, 2018.
	SHI P M, ZHANG H C, YI S Y, et al. An lmproved Feature Extraction Method of Bearing Fault SignalBased on Adaptive Multivariate Variational Mode Decomposition［J］. Acta Metrologica Sinica, 2022, 43(10):1326-1344.
［5］	SUN J D, YAN C H, WEN J T. Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning［J］. IEEE Transactions on Instrumentation and measurement, 2018, 67(1): 185-195.
	WEN J T, ZHANG P C, SUN J D, et al. Unsupervised transfer learning with residual convolutional autoencoder networks for bearing fault diagnosis［J］. China Mechanical Engineering, 2022, 33(14): 1707-1716.
［11］	孙洁娣, 刘保, 温江涛, 等. 类别标签辅助改进稠密网络的变工况轴承故障诊断［J］. 振动与冲击, 2022, 41(17): 204-212.
［16］	LONG M, ZHU H, WANG J, et al. Deep transfer learning with joint adaptation networks［C］//34th International Conference on Machine Learning, ICML 2017, 2017, 5: 3470-3479.
［20］	ZHANG X, WANG T, WANG J, et al. Pyramid Channel-based Feature Attention Network for image dehazing［J］. Computer Vision and Image Understanding, 2020, 197-198: 103003.
［26］	ZHANG W, LI C, PENG G, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load［J］. Mechanical Systems and Signal Processing, 2018, 100: 439-453.
	YUAN C Y, SUN J D, WEN J T, et al. Bearing fault diagnosis based on information fusion and improved residual dense networks［J］. Journal of Vibration and Shock, 2022, 41(4): 200-208.
［14］	TAO C, QI J, LU W, et al. Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples［J］. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-6.
［24］	SUN B, SAENKO K. Deep CORAL: Correlation alignment for deep domain adaptation［J］. Lecture Notes in Computer Science, 2016, 9915 LNCS: 443-450.
［18］	GANIN Y, USTINOVA E, AJAKAN H, et al. Domain adversarial training of neural networks［J］. The Journal of Machine Learning Research, 2016, 17(1): 2096-2030.
［23］	ZHAO Z, LI T, WU J. Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study［J］. ISA Transactions, 2020, 107: 224-255.