RAAN: A Gaussian Prior Domain Adaptive Network for Rolling Bearing Fault Diagnosis Under Variable Working Conditions
), Abstract
In the field of fault diagnosis for rolling bearings under variable working conditions, significant progress has been made using methods based on unsupervised domain adaptation (UDA). However, most existing UDA methods primarily achieve identification by directly aligning the distributions of the source and target domains, often overlooking the relevance of samples between different domains, which may result in incomplete extraction of deep features and alignment of feature distributions. Therefore, this study proposes a novel domain adaptation network based on Gaussian prior distributions, aiming at solving the challenges of cross working conditions bearing fault diagnosis. The method consists of a feature mining module and an adversarial domain adaptation module. The former effectively extracts deep features by stacking multiple residual networks (Resnet), while the latter employs an indirect latent alignment strategy, using Gaussian prior distributions in the latent feature space to indirectly align the feature distributions of the source and target domains, achieving more precise feature alignment. In addition, an adaptive factor is introduced to dynamically assess the method’s transfer and discriminative capabilities. Experimental data from two bearing systems validate that the method can effectively transfer source domain knowledge to the target domain, confirming its effectiveness.
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References
X. Li, H. Jiang, X. Xiong, and H. Shao, Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network, Mech. Mach. Theory, vol. 133, pp. 229–249, 2019.
X. Xu, S. Hu, P. Shi, H. Shao, R. Li, and Z. Li, Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm, Energy, vol. 262, p. 125342, 2023.
Q. Yao, Y. Qin, X. Wang, and Q. Qian, Multiscale domain adaption models and their application in fault transfer diagnosis of planetary gearboxes, Eng. Appl. Artif. Intell., vol. 104, p. 104383, 2021.
M. Shi, R. Zhao, Y. Wu, and T. He, Fault diagnosis of rotor based on local-global balanced orthogonal discriminant projection, Measurement, vol. 168, p. 108320, 2021.
O. Janssens, V. Slavkovikj, B. Vervisch, K. Stockman, M. Loccufier, S. Verstockt, R. Van de Walle, and S. Van Hoecke, Convolutional neural network based fault detection for rotating machinery, J. Sound Vib., vol. 377, pp. 331–345, 2016.
H. Shao, H. Jiang, H. Zhang, W. Duan, T. Liang, and S. Wu, Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing, Mech. Syst. Signal Process., vol. 100, pp. 743–765, 2018.
W. Zhang, X. Li, and Q. Ding, Deep residual learning-based fault diagnosis method for rotating machinery, ISA Trans., vol. 95, pp. 295–305, 2019.
Y. Ding, M. Jia, Q. Miao, and Y. Cao, A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings, Mech. Syst. Signal Process., vol. 168, p. 108616, 2022.
F. Jamil, T. Verstraeten, A. Nowé, C. Peeters, and J. Helsen, A deep boosted transfer learning method for wind turbine gearbox fault detection, Renew. Energy, vol. 197, pp. 331–341, 2022.
A. N. Sayed, Y. Himeur, and F. Bensaali, Deep and transfer learning for building occupancy detection: A review and comparative analysis, Eng. Appl. Artif. Intell., vol. 115, p. 105254, 2022.
J. Yu, Z. Shen, and S. Wang, Wafer map defect recognition based on deep transfer learning-based densely connected convolutional network and deep forest, Eng. Appl. Artif. Intell., vol. 105, p. 104387, 2021.
J. Zhang, Y. Wang, K. Zhu, Y. Zhang, and Y. Li, Diagnosis of interturn short-circuit faults in permanent magnet synchronous motors based on few-shot learning under a federated learning framework, IEEE Trans. Ind. Inform., vol. 17, no. 12, pp. 8495–8504, 2021.
M. Xia, H. Shao, D. Williams, S. Lu, L. Shu, and C. W. de Silva, Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning, Reliab. Eng. Syst. Saf., vol. 215, p. 107938, 2021.
Z. Chen, K. Gryllias, and W. Li, Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network, IEEE Trans. Ind. Inform., vol. 16, no. 1, pp. 339–349, 2020.
X. Li, H. Jiang, K. Zhao, and R. Wang, A deep transfer nonnegativity-constraint sparse autoencoder for rolling bearing fault diagnosis with few labeled data, IEEE Access, vol. 7, pp. 91216–91224, 2019.
Z. Wu, H. Jiang, T. Lu, and K. Zhao, A deep transfer maximum classifier discrepancy method for rolling bearing fault diagnosis under few labeled data, Knowl. Based Syst., vol. 196, p. 105814, 2020.
Z. Wu, H. Jiang, K. Zhao, and X. Li, An adaptive deep transfer learning method for bearing fault diagnosis, Measurement, vol. 151, p. 107227, 2020.
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, Domain adaptation via transfer component analysis, IEEE Trans. Neural Netw., vol. 22, no. 2, pp. 199–210, 2011.
T. Han, C. Liu, W. Yang, and D. Jiang, Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application, ISA Trans., vol. 97, pp. 269–281, 2020.
L. Guo, Y. Lei, S. Xing, T. Yan, and N. Li, Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data, IEEE Trans. Ind. Electron., vol. 66, no. 9, pp. 7316–7325, 2019.
H. Shao, M. Xia, G. Han, Y. Zhang, and J. Wan, Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images, IEEE Trans. Ind. Inform., vol. 17, no. 5, pp. 3488–3496, 2021.
L. Wen, L. Gao, and X. Li, A new deep transfer learning based on sparse auto-encoder for fault diagnosis, IEEE Trans. Syst. Man Cybern. Syst., vol. 49, no. 1, pp. 136–144, 2019.
Z. Wu, H. Jiang, S. Liu, and R. Wang, A deep reinforcement transfer convolutional neural network for rolling bearing fault diagnosis, ISA Trans., vol. 129, pp. 505–524, 2022.
K. Zhao, F. Jia, and H. Shao, A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains, Knowl. Based Syst., vol. 262, p. 110203, 2023.
X. Li, S. Cao, L. Gao, and L. Wen, A threshold-control generative adversarial network method for intelligent fault diagnosis, Complex System Modeling and Simulation, vol. 1, no. 1, pp. 55–64, 2021.
Z. Pu, D. Cabrera, C. Li, and J. V. de Oliveira, VGAN: Generalizing MSE GAN and WGAN-GP for robot fault diagnosis, IEEE Intell. Syst., vol. 37, no. 3, pp. 65–75, 2022.
L. Yang and P. Zhong, Discriminative and informative joint distribution adaptation for unsupervised domain adaptation, Knowl. Based Syst., vol. 207, p. 106394, 2020.
M. Long, Y. Cao, Z. Cao, J. Wang, and M. I. Jordan, Transferable representation learning with deep adaptation networks, IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 12, pp. 3071–3085, 2019.
K. Zhao, H. Jiang, K. Wang, and Z. Pei, Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis, Knowl. Based Syst., vol. 222, p. 106974, 2021.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, Domain-adversarial training of neural networks, J. Mach. Learn. Res., vol. 17, no. 1, pp. 2096–2030, 2016.
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