RESEARCH PAPER
A Federated Transfer Fault Diagnosis Method for Cross-Domain and Incomplete Data
1
Changshu Institute of Technology, China
Submission date: 2024-06-06
Final revision date: 2024-09-15
Acceptance date: 2024-10-06
Online publication date: 2024-10-09
Publication date: 2024-10-09
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):194182
HIGHLIGHTS
- Cross-domain federated fault diagnosis, exchanging only model parameters for privacy.
- We secure client data privacy by sharing only the parameters from local models.
- A relative distance-guided fine-tuning to improve diagnostics and avoid negative outcomes.
KEYWORDS
TOPICS
ABSTRACT
In fact, multiple clients often use similar devices and collect fault data separately, so joint multi-client collaborative fault diagnosis modeling can solve the problem of data scarcity, but this poses great challenges to data privacy protection. In this paper, we propose a federated transfer fault diagnosis method based on federated learning for cross-domain incomplete data. The proposed method only exchanges the parameters of the local training model, which achieves the privacy protection of the client’s local data. We construct a multi-client collaborative learning framework to address the problem of weak generalization ability caused by the lack of terms in single client training samples. We also propose a targeted semi-supervised fine-tuning strategy based on relative distance to reduce the probability of negative fine-tuning of out-of-distribution samples and improve the accuracy of diagnostic models. The results of cross-condition and cross-equipment experiments demonstrate that the proposed method has obvious advantages over the existing fault diagnosis methods.
FUNDING
This work was supported in part by the Suzhou Science and Technology Foundation of China under Grant SYG202021 and SYG202351, Jiangsu Provincial Natural Science Research Foundation of China under Grant 21KJA510003.
REFERENCES (45)
1.
L.Q. Xia, Y.S. Liang, P. Zheng, X. Huang, Residual-Hypergraph Convolution Network: A Model-Based and Data-Driven Integrated Approach for Fault Diagnosis in Complex Equipment, Ieee Transactions on Instrumentation and Measurement, 72 (2023).
3.
Y. Wang, W.L. Sun, L.Q. Liu, B.K. Wang, S.H. Bao, R.B. Jiang, Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin, Applied Sciences-Basel, 13 (2023).https://doi.org/10.3390/app130....
4.
Y.Y. Xiao, L.T. Chen, S.Y. Chen, Z.H. Hu, J.Y. Tang, Intelligent fault diagnosis of gear crack based on side frequency feature under different working conditions, Measurement Science and Technology, 34 (2023). https://doi.org/10.1088/1361-6....
5.
R. Wang, W.G. Huang, M.K. Shi, J. Wang, C.Q. Shen, Z.K. Zhu, Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy, Knowledge-Based Systems, 256 (2022). https://doi.org/10.1016/j.knos....
6.
W. Zhang, Z.W. Wang, X. Li, Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis, Reliability Engineering & System Safety, 229 (2023). https://doi.org/10.1016/j.ress....
7.
A. Kumar, A.M. Shaikh, Y. Li, H. Bilal, B.Q. Yin, Pruning filters with L1-norm and capped L1-norm for CNN compression, Applied Intelligence, 51 (2021) 1152-1160. https://doi.org/10.1007/s10489....
8.
Q.Z. Wang, Q. Li, K. Wang, H. Wang, P. Zeng, Efficient federated learning for fault diagnosis in industrial cloud-edge computing, Computing, 103 (2021) 2319-2337. https://doi.org/10.1007/s00607....
9.
K. Zhao, J.C. Hu, H.D. Shao, J.B. Hu, Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy, Reliability Engineering & System Safety, 236 (2023).
11.
J.B. Chen, J.P. Li, R.Y. Huang, K. Yue, Z.Y. Chen, W.H. Li, Federated Transfer Learning for Bearing Fault Diagnosis With Discrepancy-Based Weighted Federated Averaging, Ieee Transactions on Instrumentation and Measurement, 71 (2022).https://doi.org/10.1109/TIM.20....
12.
Y.H. Zhang, X.A. Xue, X.P. Zhao, L.H. Wang, Federated learning for intelligent fault diagnosis based on similarity collaboration, Measurement Science and Technology, 34 (2023). https://doi.org/10.1088/1361-6....
13.
D.Q. Geng, H.W. He, X.C. Lan, C. Liu, Bearing fault diagnosis based on improved federated learning algorithm, Computing, 104 (2022) 1-19. https://doi.org/10.1007/s00607....
14.
J.H. Du, N. Qin, D.Q. Huang, Y.M. Zhang, X.M. Jia, An Efficient Federated Learning Framework for Machinery Fault Diagnosis With Improved Model Aggregation and Local Model Training, Ieee Transactions on Neural Networks and Learning Systems, (2023).
15.
H.F. Sun, S.Q. Li, F.R. Yu, Q. Qi, J.Y. Wang, J.X. Liao, Toward Communication-Efficient Federated Learning in the Internet of Things With Edge Computing, Ieee Internet of Things Journal, 7 (2020) 11053-11067. https://doi.org/10.1109/JIOT.2....
16.
F.Y. Lu, Q.B. Tong, X.D. Jiang, Z.W. Feng, J.J. Xu, X. Wang, J.Y. Huo, A deep targeted transfer network with clustering pseudo-label learning for fault diagnosis across different Machines, Mechanical Systems and Signal Processing, 213 (2024).https://doi.org/10.1016/j.ymss....
17.
F.Y. Lu, Q.B. Tong, J.J. Xu, Z.W. Feng, X. Wang, J.Y. Huo, Q.Z. Wan, Towards multi-scene learning: A novel cross-domain adaptation model based on sparse filter for traction motor bearing fault diagnosis in high-speed EMU, Advanced Engineering Informatics, 60 (2024).https://doi.org/10.1016/j.aei.....
18.
X.H. Chen, R. Yang, Y.H. Xue, M.J. Huang, R. Ferrero, Z.D. Wang, Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016, Ieee Transactions on Instrumentation and Measurement, 72 (2023). https://doi.org/10.1109/TIM.20....
19.
C.H. Qian, J.J. Zhu, Y.H. Shen, Q.S. Jiang, Q.K. Zhang, Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge, Neural Processing Letters, 54 (2022) 2509-2531. https://doi.org/10.1007/s11063....
20.
Y.S. Zou, K.M. Shi, Y.Z. Liu, G.F. Ding, K. Ding, Rolling bearing transfer fault diagnosis method based on adversarial variational autoencoder network, Measurement Science and Technology, 32 (2021). https://doi.org/10.1088/1361-6....
21.
J.C. Kuang, G.H. Xu, T.F. Tao, Q.Q. Wu, C.C. Han, F. Wei, Domain Conditioned Joint Adaptation Network for Intelligent Bearing Fault Diagnosis Across Different Positions and Machines, Ieee Sensors Journal, 23 (2023) 4000-4010. https://doi.org/10.1109/JSEN.2....
22.
B.Y. Yang, T.T. Wang, J.S. Xie, J.S. Yang, Deep Adversarial Hybrid Domain-Adaptation Network for Varying Working Conditions Fault Diagnosis of High-Speed Train Bogie, Ieee Transactions on Instrumentation and Measurement, 72 (2023). https://doi.org/10.1109/TIM.20....
23.
Q. Hu, X.S. Si, A.S. Qin, Y.R. Lv, M. Liu, Balanced Adaptation Regularization Based Transfer Learning for Unsupervised Cross-Domain Fault Diagnosis, Ieee Sensors Journal, 22 (2022) 12139-12151. https://doi.org/10.1109/JSEN.2....
24.
S.X. Lu, Z.W. Gao, Q.F. Xu, C.X. Jiang, A.H. Zhang, X.X. Wang, Class-Imbalance Privacy-Preserving Federated Learning for Decentralized Fault Diagnosis With Biometric Authentication, Ieee Transactions on Industrial Informatics, 18 (2022) 9101-9111.
26.
C.C. Che, H.W. Wang, M.L. Xiong, X.M. Ni, Few-shot fault diagnosis of rolling bearing under variable working conditions based on ensemble meta-learning, Digital Signal Processing, 131 (2022). https://doi.org/10.1016/j.dsp.....
27.
P.Q. Wang, J.D. Li, S.B. Wang, F.S. Zhang, J.J. Shi, C.Q. Shen, A new meta-transfer learning method with freezing operation for few-shot bearing fault diagnosis, Measurement Science and Technology, 34 (2023). https://doi.org/10.1088/1361-6....
28.
X.B. Liu, H.T. Guo, Y.B. Liu, One-Shot Fault Diagnosis of Wind Turbines Based on Meta-Analogical Momentum Contrast Learning, Energies, 15 (2022). https://doi.org/10.3390/en1509....
29.
Yang, Y.F. Zhou, X. Chen, C. Li, H. Song, Fault diagnosis for wind turbines with graph neural network model based on one-shot learning, Royal Society Open Science, 10 (2023). https://doi.org/10.1098/rsos.2....
30.
Y.H. Li, K. Li, X. Liu, Y.X. Wang, L. Zhang, Lithium-ion battery capacity estimation-A pruned convolutional neural network approach assisted with transfer learning, Applied Energy, 285 (2021). https://doi.org/10.1016/j.apen....
31.
L. Cheng, X.W. Kong, J.Q. Zhang, M.Z. Yu, A Novel Adversarial One-Shot Cross-Domain Network for Machinery Fault Diagnosis With Limited Source Data, Ieee Transactions on Instrumentation and Measurement, 71 (2022). https://doi.org/10.1109/TIM.20....
32.
X.L. Zhang, Z.Q. Su, X.L. Hu, Y. Han, S.X. Wang, Semisupervised Momentum Prototype Network for Gearbox Fault Diagnosis Under Limited Labeled Samples, Ieee Transactions on Industrial Informatics, 18 (2022) 6203-6213. https://doi.org/10.1109/TII.20....
33.
C.X. Jiang, H. Chen, Q.F. Xu, X.X. Wang, Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks, Journal of Intelligent Manufacturing, 34 (2023) 1667-1681. https://doi.org/10.1007/s10845....
34.
Z.F. Xu, M. Bashir, Q.S. Liu, Z.F. Miao, X.Y. Wang, J. Wang, N. Ekere, A novel health indicator for intelligent prediction of rolling bearing remaining useful life based on unsupervised learning model, Computers & Industrial Engineering, 176 (2023). https://doi.org/10.1016/j.cie.....
35.
Zhang, J.Y. Jiao, J. Lin, H. Li, J.D. Hua, D. He, Uncertainty-based contrastive prototype-matching network towards cross-domain fault diagnosis with small data, Knowledge-Based Systems, 254 (2022). https://doi.org/10.1016/j.knos....
36.
X.P. Zhao, M.Y. Ma, F. Shao, Bearing fault diagnosis method based on improved Siamese neural network with small sample, Journal of Cloud Computing-Advances Systems and Applications, 11 (2022). https://doi.org/10.1186/s13677....
37.
R.J. Hou, Z.Y. Chen, J.L. Chen, S.L. He, Z.T. Zhou, Imbalanced fault identification via embedding-augmented Gaussian prototype network with meta-learning perspective, Measurement Science and Technology, 33 (2022). https://doi.org/10.1088/1361-6....
38.
Y.C. Zhang, K. Feng, H. Ma, K. Yu, Z.H. Ren, Z. Liu, MMFNet: Multisensor Data and Multiscale Feature Fusion Model for Intelligent Cross-Domain Machinery Fault Diagnosis, Ieee Transactions on Instrumentation and Measurement, 71 (2022). https://doi.org/10.1109/TIM.20....
39.
Q.B. Wang, Y.B. Xu, S.K. Yang, J.T. Chang, J.G. Zhang, X.G. Kong, A domain adaptation method for bearing fault diagnosis using multiple incomplete source data, Journal of Intelligent Manufacturing, (2023). https://doi.org/10.1007/s10845....
40.
B. Yang, Y.G. Lei, X. Li, C. Roberts, Deep Targeted Transfer Learning Along Designable Adaptation Trajectory for Fault Diagnosis Across Different Machines, Ieee Transactions on Industrial Electronics, 70 (2023) 9463-9473. https://doi.org/10.1109/TIE.20....
42.
J. Lin, H.D. Shao, Z.S. Min, J.J. Luo, Y.M. Xiao, S. Yan, J. Zhou, Cross-domain fault diagnosis of bearing using improved semi-supervised meta-learning towards interference of out-of-distribution samples, Knowledge-Based Systems, 252 (2022). https://doi.org/10.1016/j.knos....
43.
R. Wang, F.C. Yan, L. Yu, C.Q. Shen, X. Hu, J. Chen, A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis, Mechanical Systems and Signal Processing, 198 (2023). https://doi.org/10.1016/j.ymss....
44.
Z.X. Zhou, H. Wang, Z.X. Li, W. Chen, Fault diagnosis of rolling bearing based on deep convolutional neural network and gated recurrent unit, Journal of Advanced Mechanical Design Systems and Manufacturing, 17 (2023). https://doi.org/10.1299/jamdsm....
45.
C. Lessmeier, KAt-DataCenter, KAt-DataCenter, KAt-DataCenter, pp. KAt-DataCenter.
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