Duan Xianling, Hu Shaolin, Wang Sijing, Duan Ru
School of Information and Control Engineering Jilin Institute of Chemical Technology, Jilin, Jilin, 132000, China.
Automation School Guangdong University of Petrochemical Technology, Maoming, Guangdong, 52500, China.
Heliyon. 2024 Aug 23;10(17):e36611. doi: 10.1016/j.heliyon.2024.e36611. eCollection 2024 Sep 15.
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.
压缩机是石油化工行业重要的生产设备，其故障诊断的准确性至关重要。为了及时检测和诊断压缩机设备故障，本文构建了一种深度残差收缩视觉网络（DRS-ViT）。该网络由改进的残差网络（ResNet）和视觉Transformer（ViT）组成。利用格拉姆角场（GAF）将获取的压缩机振动信号转换为格拉姆角和场（GASF）图。所得图像通过改进的ResNet网络提取初始特征。随后将提取的特征图像输入到ViT模型中进行故障分类。实验结果表明，DRS-ViT模型实现的故障诊断准确率为99.5%。模型的可视化表明它能够有效识别故障点。通过与各种模型的比较分析，证实了DRS-ViT模型的有效性和鲁棒性。
