Research on fault diagnosis of industrial robots based on generative adversarial network

Abstract

Abstract-In today's industrial production, companies have to introduce various types of industrial robots in order to reduce labor costs, shorten product processing time, and obtain greater profits. This move has caused the installed capacity of industrial robots to increase year by year. However, this also makes industrial robots have more and more frequent failures and an increasing amount of failures in actual production. The occurrence of industrial robot failures is unavoidable, and the amount of data in the healthy state and the amount of data in the fault state are extremely unbalanced. Whether the fault diagnosis model is efficient and accurate is the key to providing reliable theoretical and technical support for the health management of industrial robots. Therefore, this paper constructs an imbalanced data fault diagnosis model based on Wasserstein Generative Adversarial Network (WGAN) for fault diagnosis of industrial robots in data balanced and unbalanced states. We evaluate the quality of generated data through the following three aspects: the change in loss values ​​of the discriminator and generator, the degree of overlap between the generated data and real data samples, and the classification accuracy of the classification algorithm. The results show that the data generated by the WGAN model is of high quality and can be used as extended data for the fault data set. Next, we used the deep residual network designed in this article to perform fault diagnosis on the balanced data set expanded with the generated data. The experimental results confirmed the high quality of the generated data and the outstanding expressive ability of the deep residual network. In order to measure the performance of the model in this paper, we used 7 types of generative adversarial networks to generate fault data. The quality of the generated data samples is still analyzed and evaluated through the three aspects mentioned above. The results show that only the conditional Wasserstein generative adversarial network model generates data of high quality, which can be used as extended data for fault data and has good results in actual applications.