Composite Fault Diagnosis Based on Deep Convolutional Generative Adversarial Network

Abstract

In the field of fault diagnosis, composite fault are difficult to diagnose accurately because of the coupling effect between various faults and dynamic working conditions (load, rotating speed, etc.). In addition, fault samples under specific operating conditions of practical manufacturing processes suffer from inadequacy and unbalanced distribution. Aiming at the problem of sample imbalance in the process of model building, this paper takes the bearing and gear of gearbox as the research object and presents a fault diagnosis method of the gearbox with less sample information. A Deep Convolutional Generative Adversarial Network model (DCGAN) is constructed for a more accurate fault diagnosis, where the counter-learning mechanism is used to train the generator to learn the distribution characteristic of the original fault samples and to generate additional fault samples. Fault samples under various working conditions were collected through the designed experimental platform for the training of the fault diagnosis model. The experiments show that the DCGAN fault diagnosis model can effectively improve fault diagnosis accuracy with insufficient diagnosis samples.