Fault diagnosis of gearbox with small-sample based on SCACGAN

A new method for gearbox fault diagnosis based on the self-correcting auxiliary classifier generative adversarial networks (SCACGAN) is suggested in response to the limited diversity and low quality of fault samples produced by the auxiliary classifier generative adversarial networks (ACGAN) during the small-sample gearbox fault diagnosis process, which subsequently results in low diagnostic accuracy. Firstly, an independent classifier is introduced into the auxiliary classifier generative adversarial network to mitigate the adverse impact of discriminator output errors on the quality of generated samples, and to classify the health status of different gearbox samples. Secondly, the problem of low-quality generated samples during the training phase is addressed by using the least squares function to improve the model’s generation and classification skills. Lastly, a self-correcting convolutional neural network is integrated into the generator to enhance the capability of fault feature acquisition. Experimental results demonstrate that under small-sample conditions, the proposed approach is capable of generating higher-quality fault samples, thereby improving the accuracy of gearbox fault diagnosis.