Fault Diagnosis of Tractor Transmission System Based on Time GAN and Transformer

Abstract

The transmission system of a tractor is a crucial component, so it is crucial to promptly identify and correctly diagnose faults in it. However, due to the limited samples of faults occurring during its operational processes, employing existing fault diagnosis methods directly yields unsatisfactory results. This paper proposes a fault diagnosis model combining Time Generative Adversarial Networks (Time GAN) and Transformer. To enhance diagnostic accuracy, we first employ Time GAN for data augmentation, addressing the issue of imbalanced fault samples in practical scenarios. Then, we integrate a Transformer network with improved multi-head self-attention mechanisms, leveraging the advantages of the Transformer’s encoder-decoder architecture and attention mechanism to enhance diagnostic performance. Bearing data from Case Western Reserve University (CWRU) was used to validate the diagnostic performance of the proposed model, while gear data from an experimental rig built by the author was used to validate the model’s generalization capability. Experimental results indicate that the accuracy reached 98.96% and 95.36% in CWRU Dataset and Self-made Dataset respectively. In strong noise environments, the accuracy remains above 93%. In conclusion, the diagnostic model presented in this paper can reliably diagnose tractor transmission system problems in few-sample conditions and noise environments compared to traditional machine learning models.

Keywords

• Transformer model
• Time GAN
• data augmentation
• fault diagnosis