Abstract
Bearing fault diagnosis can be used to accurately and automatically identify the type and severity of faults. Federation learning can perform learning without transferring local data among multiple local nodes with the same data features. The traditional federated learning algorithm is difficult to identify high-quality local models among class-unbalanced local nodes, which leads to the poor quality of the training model and the slow training speed. To improve the quality and training speed of the model, this paper proposes the FA-FedAvg algorithm for fault diagnosis based on the traditional federated learning algorithm. Specifically, the weighting strategy of the model is optimized, which is conducive to increasing the weight of high-quality local models, thereby improving the quality of training models. Then, a model aggregation strategy based on precision difference is proposed to reduce the number of iterations and accelerate the convergence of the training model. Finally, the proposed algorithm is compared with FedAvg and FedProx algorithms under different data distribution conditions. The experimental results show that, compared with the comparison algorithm, the number of model training iterations of the FA-FedAvg algorithm is reduced by 52.5% on average, and the fault classification accuracy has an average increment of about 8.6%. Moreover, the FA-FedAvg fault diagnosis method is robust under different classes and data volumes.
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References
Niu Y, Fei J, Li Y, Wu D (2020) A novel fault diagnosis method based on EMD, cyclostationary, SK and TPTSR. J Mech Sci Technol 34:1925–1935. https://doi.org/10.1007/s12206-020-0414-y
Yu Y, YuDejie JC (2006) A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J Sound Vib 294:269–277. https://doi.org/10.1016/j.jsv.2005.11.002
Ni J, Zhang C, Yang SX (2011) An adaptive approach based on KPCA and SVM for real-time fault diagnosis of HVCBs. IEEE Trans Power Deliv 26:1960–1971. https://doi.org/10.1109/TPWRD.2011.2136441
Shi H, Guo L, Tan S, Bai X (2019) Rolling bearing initial fault detection using long short-term memory recurrent network. IEEE Access 7:171559–171569. https://doi.org/10.1109/ACCESS.2019.2954091
Li J, Li X, He D (2019) A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction. IEEE Access 7:75464–75475. https://doi.org/10.1109/ACCESS.2019.2919566
Liu Q, Huang C (2019) A fault diagnosis method based on transfer convolutional neural networks. IEEE Access 7:171423–171430. https://doi.org/10.1109/ACCESS.2019.2956052
Wu Y, Jiang B, Lu N (2019) A descriptor system approach for estimation of incipient faults with application to high-speed railway traction devices. IEEE Trans Syst Man Cybern Syst 49:2108–2118. https://doi.org/10.1109/TSMC.2017.2757264
Wu Y, Jiang B, Wang Y (2020) Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains. ISA Trans 99:488–495. https://doi.org/10.1016/j.isatra.2019.09.020
Tao F, Qi Q, Wang L, Nee AYC (2019) Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering 5:653–661. https://doi.org/10.1016/j.eng.2019.01.014
Chen Y, Sun X, Jin Y (2019) Communication-efficient federated deep learning with Layerwise asynchronous model update and temporally weighted aggregation. IEEE Trans Neural Networks Learn Syst 1:10. https://doi.org/10.1109/tnnls.2019.2953131
Hard A, Partridge K, Nguyen C, et al (2020) Training keyword spotting models on non-iid data with federated learning. arXiv:10.21437https://doi.org/10.21437/interspeech.2020-3023
Brendan McMahan H, Moore E, Ramage D et al (2017) Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th international conference on artificial intelligence Stat AISTATS, p 54
Li X, Yang W, Huang K, et al (2019) On the convergence of FedAvg on Non-IID Data. arXiv 1–26
Hao M, Li H, Luo X et al (2019) Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Trans Ind Inform 3203:1–1. https://doi.org/10.1109/tii.2019.2945367
Han Y, Li D, Qi H et al (2019) Federated learning-based computation offloading optimization in edge computing-supported internet of things. ACM Int Conf Proc Ser. https://doi.org/10.1145/3321408.3321586
Nguyen TD, Marchal S, Miettinen M, et al (2019) D\(\ddot{{\rm I}}\)oT: A federated self-learning anomaly detection system for IoT. In: Proceedings-international conference on distributed computing systems, pp 756– 767. https://doi.org/10.1109/ICDCS.2019.00080
Lu X, Liao Y, Lio P, Hui P (2020) Privacy-preserving asynchronous federated learning mechanism for edge network computing. IEEE Access 8:48970–48981. https://doi.org/10.1109/ACCESS.2020.2978082
Duan M, Liu D, Chen X, et al (2019) Astraea: self-balancing federated learning for improving classification accuracy of mobile deep learning applications. In: Proceedings of the 2019 IEEE international conference on computer design ICCD 2019, pp 246–254. https://doi.org/10.1109/ICCD46524.2019.00038
Sun H, Li S, Yu FR et al (2020) Towards communication-efficient federated learning in the internet of things with edge computing. IEEE Internet Things J 14:1–1. https://doi.org/10.1109/jiot.2020.2994596
Liu Y, Garg S, Nie J et al (2020) Deep anomaly detection for time-series data in industrial IoT: a communication-efficient on-device federated learning approach. 4662:1–11. https://doi.org/10.1109/JIOT.2020.3011726
Feng C, Wang Y, Zhao Z, et al (2020) Joint optimization of data sampling and user selection for federated learning in the mobile edge computing systems. In: Proceedings of IEEE international conference on communications workshops, pp 1–6. https://doi.org/10.1109/ICCWorkshops49005.2020.9145182
Bonawitz K, Salehi F, Konecny J, et al (2019) Federated learning with autotuned communication-efficient secure aggregation. In: Conf Rec - Asilomar Conf Signals, Syst Comput 2019–Novem: 1222–1226. https://doi.org/10.1109/IEEECONF44664.2019.9049066
Luo F, Zhang L, Du B, Zhang L (2020) Dimensionality reduction with enhanced hybrid-graph discriminant learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 58:5336–5353. https://doi.org/10.1109/TGRS.2020.2963848
Luo F, Huang H, Duan Y et al (2017) Local geometric structure feature for dimensionality reduction of hyperspectral imagery. Remote Sens 9:1–23. https://doi.org/10.3390/rs9080790
Li T, Sahu AK, Zaheer M, et al (2020) Federated optimization in heterogeneous networks. arXiv:1812.06127v5
Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: 3rd Internatinal Confernce Learning Represent ICLR 2015-Confernce Track Proceedings, pp 1–15
Liu W, Chen L, Chen Y, Zhang W (2020) Accelerating federated learning via momentum gradient descent. IEEE Trans Parallel Distrib Syst 31:1754–1766. https://doi.org/10.1109/TPDS.2020.2975189
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This work was supported in part by the National Key R&D Program of China under Grant 2017YFE0123000, and in part by the Project of Technological Innovation and Application Development of Chongqing Science and Technology Commission of China under Grant cstc2018jszx-cyzd0078
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Geng, D., He, H., Lan, X. et al. Bearing fault diagnosis based on improved federated learning algorithm. Computing 104, 1–19 (2022). https://doi.org/10.1007/s00607-021-01019-4
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DOI: https://doi.org/10.1007/s00607-021-01019-4
Keywords
- Bearing fault diagnosis
- Federated learning
- Convolutional neural network (CNN)
- Federated averaging (FedAvg)