Mechanical and electrical equipment fault diagnosis based on dual attention mechanism and S-BiGAN

Abstract

The accurate fault diagnosis of mechanical and electrical equipment under the condition of limited label samples is of great significance for improving the health management ability of complex mechanical and electrical equipment. In response to the problem of difficulty in establishing accurate fault diagnosis models under the condition of limited label samples, the attention module is introduced into the generative adversarial network based on semi-supervised generative adversarial network, in which the Gramian angular field (GAF) is used to convert onedimensional data into two-dimensional images. In combination with the characteristics of bidirectional generative adversarial network, a semi-supervised bidirectional generative adversarial network (S-BiGAN) based on dual attention mechanism for fault diagnosis of electromechanical equipment is proposed, and the bearing data is taken as an example for verification. The results show that, compared with algorithms such as CNN-SVM and SGAN, the proposed model can improve the quality of sample generation and fault classification features, effectively solve the fault diagnosis problem in the case of fewer label samples, and greatly improve the accuracy of fault diagnosis.

Keywords

• mechanical and electrical equipment
• dual attention mechanism
• adversarial neural network
• unsupervised learning
• fault diagnosis