ICoT-GAN: Integrated Convolutional Transformer GAN for Rolling Bearings Fault Diagnosis Under Limited Data Condition

Abstract

Limited operating data resulting from complex and changeable working conditions significantly undermines the performance of deep learning-based methods for rolling bearing fault diagnosis. Generally, this problem can be solved by using the generative adversarial network (GAN) to augment data. However, most GAN-based methods seldom comprehensively consider the global interactions and local dependencies in raw vibration signals during data generation, leading to a decline in the quality of the generated data and compromising the diagnostic accuracy. To address this problem, a fault diagnosis method based on integrated convolutional transformer GAN (ICoT-GAN) is proposed to improve the diagnostic performance under limited data condition by generating high-quality signals. Firstly, a new data augmentation model ICoT-GAN is developed. In ICoT-GAN, a novel ICoT block is designed to construct the generator and discriminator. The ICoT block achieves the integration of attention-based global information capture and convolution-based local feature extraction through the incorporation of convolution within the transformer encoder. This design allows the ICoT-GAN to comprehensively extract both global and local time-series features of raw signals and generate high-quality signals. Secondly, a novel data evaluation indicator, referred to as the Multiple Time-Domain Features Indicator (MTFI), is designed to quantitatively evaluate generated signals’ quality by calculating the similarity of time-domain features between the real and generated signals. The MTFI can complement probability distribution indicators and provide a comprehensive evaluation of the data augmentation model’s generation ability by considering both time-series feature similarity and probability distribution differences. Finally, the effectiveness of our proposed method has been successfully demonstrated under limited data condition using the CWRU and IMS bearing datasets. With only 16 training samples per class, our proposed method achieves diagnostic accuracy of 99.99% and 99.70%, respectively. Additionally, the data generation time is only 1163.31s, indicating the efficiency of our proposed method.