Abstract
Meta-learning methods have been widely applied to solve few-shot problems. However, the metamodels of current meta-learning methods may be too biased toward the tasks in the meta-training phase and are less adaptable to new tasks, especially when the number of new tasks is small. To reduce the bias of the metamodel and improve its generalizability, this paper proposes a Task-Agnostic Generalized Meta-Learning (TAGML) algorithm based on Model-Agnostic Meta-Learning (MAML) for few-shot bearing fault diagnosis. The algorithm improves MAML in terms of both network structure and optimization algorithm. Firstly, the quality of feature extraction is improved by adding a squeeze-and-excitation attention module to the network of MAML. Secondly, the following improvements are made in the optimization algorithm: (1) The stability of the training process is improved by using multi-step loss optimization in the optimization; (2) It is proposed to add the Task-Agnositic regular penalty term to the meta-optimization objective function to improve the task unbiasedness of the metamodel; (3) To speed up the convergence and further improve the model’s ability to generalize to different tasks, an iterative updatable outer loop learning rate strategy is used. Experiments demonstrate that the algorithm is not only effective in identifying new fault tasks that do not appear in the meta-training phase but also has good recognition performance for generalized bearing fault scenarios with a mixture of seen and unseen class fault tasks.
Supported in part by Central Funds Guiding the Local Science and Technology Development (Basic Research Projects) (206Z5001G), Hebei Natural Science Foundation (F2019203583), and Hebei Key Laboratory Project (202250701010046).
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References
Zhang, T., Chen, J., Li, F., Zhang, K., Lv, H., He, S., Xu, E.: Intelligent fault diagnosis of machines with small & imbalanced data: a state-of-the-art review and possible extensions. ISA transactions (2021)
Li, Z., Zhou, F., Chen, F., Li, H.: Meta-sgd: Learning to learn quickly for few shot learning. ArXiv abs/1707.09835 (2017)
Vinyals, O., Blundell, C., Lillicrap, T.P., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: NIPS (2016)
Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. ArXiv abs/1703.05175 (2017)
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2017)
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, pp. 1842–1850. PMLR (2016)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning (2017)
Feng, Y., Chen, J., Xie, J., Zhang, T., Lv, H., Pan, T.: Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: algorithms, applications, and prospects. Knowl. Based Syst. 235, 107646 (2021)
Jamal, M.A., Qi, G.J., Shah, M.: Task agnostic meta-learning for few-shot learning. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11711–11719 (2018)
Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. ArXiv abs/1803.00676 (2018)
Zhang, S., Ye, F., Wang, B., Habetler, T.G.: Few-shot bearing fault diagnosis based on model-agnostic meta-learning. IEEE Trans. Ind. Appl. 57, 4754–4764 (2020)
Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2011–2023 (2017)
Feng, Y., Chen, J., Zhang, T., He, S., Xu, E., Zhou, Z.: Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis. ISA transactions (2021)
Antoniou, A., Edwards, H., Storkey, A.J.: How to train your maml. ArXiv abs/1810.09502 (2018)
Smith, W.A., Randall, R.B.: Rolling element bearing diagnostics using the case western reserve university data: a benchmark study. Mech. Syst. Signal Process. 64, 100–131 (2015)
Lessmeier, C., Kimotho, J.K., Zimmer, D., Sextro, W.: Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification (2016)
Wu, J., Zhao, Z., Sun, C., Yan, R., Chen, X.: Few-shot transfer learning for intelligent fault diagnosis of machine. Measurement 166, 108202 (2020)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014)
Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv: Learning (2016)
Wang, D., Zhang, M., Xu, Y., Lu, W., Yang, J., Zhang, T.: Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions. Mechanical Systems and Signal Processing (2021)
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Yang, X., Zhang, L., Wang, J. (2023). Task-Agnostic Generalized Meta-learning Based on MAML for Few-Shot Bearing Fault Diagnosis. In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14355. Springer, Cham. https://doi.org/10.1007/978-3-031-46305-1_10
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DOI: https://doi.org/10.1007/978-3-031-46305-1_10
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