Intelligent fault identification for industrial automation system via multi-scale convolutional generative adversarial network with partially labeled samples

Abstract

Rolling bearings are the widely used parts in most of the industrial automation systems. As a result, intelligent fault identification of rolling bearing is important to ensure the stable operation of the industrial automation systems. However, a major problem in intelligent fault identification is that it needs a large number of labeled samples to obtain a well-trained model. Aiming at this problem, the paper proposes a semi-supervised multi-scale convolutional generative adversarial network for bearing fault identification which uses partially labeled samples and sufficient unlabeled samples for training. The network adopts a one-dimensional multi-scale convolutional neural network as the discriminator and a multi-scale deconvolutional neural network as the generator and the model is trained through an adversarial process. Because of the full use of unlabeled samples, the proposed semi-supervised model can detect the faults in bearings with limited labeled samples. The proposed method is tested on three datasets and the average classification accuracy arrived at of 100%, 99.28% and 96.58% respectively Results indicate that the proposed semi-supervised convolutional generative adversarial network achieves satisfactory performance in bearing fault identification when the labeled data are insufficient.