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Toward Small Sample Challenge in Intelligent Fault Diagnosis: Attention-Weighted Multidepth Feature Fusion Net With Signals Augmentation

Intelligent fault diagnosis of machines has yielded fruitful achievements; however, the application in engineering scenarios is still not satisfactory. There are two reasons: 1) lacking fault signals of machines to support the training of diagnosis models and 2) difficulty in sensitive fault feature extraction from raw signals. In view of these, an attention-weighted multidepth feature fusion scheme with signals augmentation is proposed. In the proposed scheme, generative adversarial networks (GANs) are applied to expand the fault training samples set, which helps improve the generalization ability of the diagnosis model. Then, we design a multidepth feature fusion net based on a deep convolutional neural network for sensitive feature learning from raw signals. Since features of different depths may differ in their ability to represent faults, the attention mechanism is utilized to adaptively assign weights to features of different depths based on the real-time training results of the model, thus achieving weighted fusion of multidepth features. The experimental results indicate that the proposed scheme can achieve high diagnostic accuracy with only a small number of fault training samples. Moreover, the attention module in the scheme is proved to be effective in sensitive fault feature learning in two-bearing diagnosis cases.