A multi-branch redundant adversarial net for intelligent fault diagnosis of multiple components under drastically variable speeds

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Intelligent fault diagnosis with small training samples plays an important role in the safety of mechanical equipment. However, affected by sharp speed variation, fault feature is extremely weak, which raises difficulty for fault diagnosis. The mutual coupling of multi-component fault features further increases the difficulty. Considering the ability of redundant second generation wavelet transform in non-stationary feature extraction, a multi-branch redundant adversarial net (RedundancyNet) is proposed to address the above issues. The Net consists of discriminator, the generator based on redundant reconstruction, and the classifier based on redundant decomposition. Firstly, through adversarial training process, the generator fuses multi-scale features to generate the signal with varying speeds, thereby expanding training data. Secondly, through layer-by-layer multi-resolution feature enhancement, the classifier boosts weak fault features of vibration signals at variable speeds. Finally, a multi-branch framework is proposed to realize multi-component fault location and damage identification. The proposed method is validated on two cases. The average classification accuracy in the two cases reach 97.14% and 98.33% respectively. However, other end-to-end intelligent fault diagnosis methods for varying speeds or small samples can only reach the highest classification accuracy of 95.14% in Case 1 and 93.59% in Case2, which is much less than RedundancyNet. The analysis results highlight the effectiveness of the net under drastically variable speeds and small faulty training samples. Besides, the proposed classifier is easy to understand, which reveals the process of feature learning and the extracted feature under varying speeds.