Abstract
Rolling bearings are widely used in industrial manufacturing, and ensuring their stable and effective fault detection is a core requirement in the manufacturing process. However, it is a great challenge to achieve a highly accurate rolling bearing fault diagnosis because of the severe imbalance and distribution differences in fault data due to weak early fault features and interference from environmental noise. An intelligent fault diagnosis strategy for rolling bearings based on grayscale image transformation, a generative adversative network, and a convolutional neural network was proposed to solve this problem. First, the original vibration signal is converted into a grayscale image. Then more training samples are generated using GANs to solve severe imbalance and distribution differences in fault data. Finally, the rolling bearing condition detection and fault identification are carried out by using SECNN. The availability of the method is substantiated by experiments on datasets with different data imbalance ratios. In addition, the superiority of this diagnosis strategy is verified by comparing it with other mainstream intelligent diagnosis techniques. The experimental result demonstrates that this strategy can reach more than 99.6% recognition accuracy even under substantial environmental noise interference or changing working conditions and has good stability in the presence of a severe imbalance in fault data.
Highlights
Rolling bearings are widely used in industrial manufacturing
The choice of applying Wasserstein generative adversarial network (WGAN)-GP to force the discriminator to satisfy the continuity constraint of the 1-Lipschitz function by adding a gradient penalty term results in faster convergence and better quality of generated samples; The attention mechanism was introduced into the field of bearing fault diagnosis, and the self-attentive convolutional neural network (SECNN) was constructed, which can automatically extract information related to deep fault features and further improve the anti-interference ability and classification accuracy of the model for unbalanced data; This method has outstanding performances in domain adaptation and can gain satisfactory diagnostic performance even when the working environment changes or the environmental noise is strong
An intelligent fault diagnosis method based on WGAN-GP and SECNN is proposed for rolling bearing fault diagnosis analysis under severe imbalance and distribution discrepancy of fault data
Summary
Rolling bearings are widely used in industrial manufacturing. Ensuring the safe and stable operation of rolling bearings is the core requirement of the manufacturing process, and their health condition has a significant impact on system dependability, productivity, and facility lifetime [1,2,3]. The choice of applying WGAN-GP to force the discriminator to satisfy the continuity constraint of the 1-Lipschitz function by adding a gradient penalty term results in faster convergence and better quality of generated samples; The attention mechanism was introduced into the field of bearing fault diagnosis, and the self-attentive convolutional neural network (SECNN) was constructed, which can automatically extract information related to deep fault features and further improve the anti-interference ability and classification accuracy of the model for unbalanced data; This method has outstanding performances in domain adaptation and can gain satisfactory diagnostic performance even when the working environment changes or the environmental noise is strong.
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