Domain adaptive networks with limited data for rotating machinery fault diagnosis: a case of study of gears

Abstract Rotating machinery is one of the most common components in the industry. Therefore, timely and accurate fault diagnosis of rotating machinery is essential for the regular operation of equipment. At present, some achievements have been made in rotating machinery fault diagnosis based on a large number of marked fault data. However, most of the machines are in a normal state in actuality. Especially, the machines run under different loads, so it is costly to collect a large number of labeled fault data under different load distributions. To solve rotating machinery fault diagnosis in different load conditions with limited samples, a domain adaptive group convolutional neural network is proposed. Firstly, the least squares generative adversarial networks were used to expand the limited target sample data. By changing the objective function, the two defects of the low quality of the vibration signal generated by the traditional generative adversarial networks and the unstable training process are optimized. Secondly, the raw vibration signals in the source domain are pre-trained by the group convolutional neural network, and the group training network effectively reduces network parameters. Finally, the source domain signals and target domain signals were trained in domain adversarial networks to diagnose different distributed data in target domains. The proposed method is validated by collecting the raw vibration signals of gears under different loads and different health states, and the effectiveness of the proposed method is proved. Experimental validation shows that the method proposed in this paper achieves an average accuracy improvement of more than 12% compared to other existing methods.