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Not applicable; b) Minimally intensive layer delamination; c) Vacuum assisted filtration.

In practical applications, it is difficult to obtain enough fault samples to train the fault classification model of rolling bearings, and the specifications of bearings used in different mechanical equipment may be different. The diagnosis model trained on a certain specification of bearings may not be applicable to other specification of bearings. To solve the above problems, a few-shot rolling bearing fault classification method based on improved relation network is proposed. Firstly, Fourier transform is applied to vibration signals of different specifications of bearings. The data of different specifications of bearings are divided into meta-train set and meta-test set according to the meta-learning training strategy, and each set is further divided into support set and query set. Secondly, an improved relation network is built. The residual shrinkage module and the SELU activation function are introduced into the embedding module of the relation network. The improved embedding module is used to extract the sample features of the support set and query set, and then the features of the two are combined and input into the relation module to get the relation score. The query set samples are classified according to the score. Finally, the rolling bearing fault classification model is obtained after multiple episodes. The experimental results show that, compared with the partial transfer learning and meta-learning methods, the proposed method only needs a few or even a single sample to achieve the fault classification of different specifications of rolling bearings under different loads. In the case of one-shot, the average classification accuracy can reach 93.3%.