Optimization of W-DCGAN Fault Diagnosis Method Based on Self-attention Mechanism

Abstract Bearing component is the most important component of wind turbine, the harsh operating environment makes the bearing prone to failure, and the maintenance cost is very expensive when a failure occurs. Complex environmental noise brings serious noise pollution to fault samples. In addition, the serious sample imbalance between fault samples and normal samples brings great challenges to bearing fault diagnosis. In view of the above mentioned problems, this paper uses a self-attention mechanism optimization and Wasserstein distance improvement deep convolutional adversarial network model based on self-attention mechanism optimization and Wasserstein distance improvement deep Bearing fault diagnosis method based on the convolution generative Adversarial Network Model (SAW-DCGAN). The experimental results show that W-DCGAN has excellent generating ability and can generate samples like real samples to achieve the balance of samples. The addition of self-attention makes the classification features more expressive, and can accelerate the training speed of the model while having a higher fault diagnosis rate, which verifies the practicability of the method.