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To solve the problem that cross-domain fault diagnosis cannot be performed due to the distribution differences between different working conditions, a transfer diagnosis method based on multi-representation adversarial neural network is proposed. First, the multi-representation neural network is applied to extract multiscale features. Second, the domain adversarial network is utilized to set the gradient inversion layer and extract the domain invariant features in the multiscale features. In terms of the loss function, the Wasserstein function and cross-entropy loss function are utilized to measure the distance between the source domain and the target domain. 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