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Fault Diagnosis of Rotating Machinery Under Multiple Operating Conditions Generalization: A Representation Gradient Muting Paradigm

Fault diagnosis of rotating machinery under fixed operating conditions has been developed considerably. However, the designed model suffers from a performance loss due to the internal covariance shift caused by the changes in operating conditions. How to mine the essential characteristics of faults by multiple related domains and then generalize well to unseen operating conditions is of great significance for the fault diagnosis of rotating machinery. To this end, we present a generic representation gradient muting paradigm (RGMP) to enhance the generalization ability of the diagnostic model. We first develop a deep convolutional network with skip connection and batch normalization. Then, RGMP guides the network to purposely discard a small number of dominant features activated by the training samples and forces the network to learn the remaining label-related representations. RGMP facilitates the network to activate more broad perceptual fields for out-of-domain samples without prior knowledge of the unseen conditions. Finally, the fine-tuned representations are fed into the classification layer, and the network parameters are optimized by backpropagation. Experiments on gear and high-speed train bearing verify the effectiveness of the proposed model under unseen operating conditions.