Compressor fault diagnosis and result visualization based on fusion of vision transformer and improved residual network

Abstract

Compressors are important production equipment in the petrochemical industry, and the accuracy of their fault diagnosis is critical. In order to detect and diagnose compressor equipment faults in a timely manner, this paper constructs a deep residual shrinkage visual network (DRS-ViT). The network comprises a modified residual network (ResNet) and a vision transformer (ViT). The obtained compressor vibration signals were transformed into gram angle sum field (GASF) plots using gram angle field (GAF). The resulting image is the passed through a modified ResNet network to extract initial features. The extracted feature images are subsequently input into the ViT model for fault classification. The experimental results demonstrate that the fault diagnosis accuracy achieved by the DRS-ViT model is 99.5 %. The visualization of the model indicates that it can effectively identify the fault points. The validity and robustness of the DRS-ViT model are confirmed through comparison and analysis with various models.