Data-driven-based intelligent fault diagnosis has achieved significant success. However, the limited number of features and samples often hinders the accuracy of such diagnoses. On the one hand, addressing the issue o...
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Data-driven-based intelligent fault diagnosis has achieved significant success. However, the limited number of features and samples often hinders the accuracy of such diagnoses. On the one hand, addressing the issue of low accuracy in intelligent fault diagnosis due to limited features, and the additional complexity of intelligent fault diagnosis algorithms makes people worry about the rationality and interpretability of the decisions made by the model, benefiting from the Taylor series expansion theorem, we propose an intelligent fault diagnosis method based on an autoencoder-multidimensional Taylor network (MTN). On the other hand, to address the problem of low accuracy of intelligent fault diagnosis due to small samples, we introduce federated learning. In federated learning, multiclient data is pooled to improve the accuracy of intelligent fault diagnosis. However, the interaction information between the client and the federation center is easily stolen by eavesdroppers. For this reason, to protect the security of the information interaction between the federated centers and the clients, we propose a hybrid additive and multiplicative coding strategy based on punishing eavesdroppers. Finally, we validate the new method based on the actual flexible rotor and the open-bearing dataset of Western Reserve University. The experimental results show that the intelligent fault detection model proposed in this article can improve the accuracy of fault detection, while the proposed information protection strategy can realize the effect of punishing eavesdroppers. The proposed method in this article is further compared with the latest research methods to verify the advancement of the proposed method.
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