LAUSR

Industrial robots are one of the most typical machines in smart manufacturing systems. Their joint bearing faults account for a significant portion of failures. Data-driven bearing fault diagnosis methods, especially deep learning methods, have become a research hotspot due to the development of the industrial Internet of Things and big data. However, the varying working conditions of industrial robots, such as the continuous changing of load and speed, challenge the existing data-driven methods. Although adversarial-based domain adaptive methods are promising for solving this problem, they still face an equilibrium issue in the model training process. Therefore, a novel deep perceptual adversarial domain adaptive (DPADA) method is proposed for fault diagnosis of industrial robot bearings under varying conditions in this article. Here, a novel perceptual loss is proposed to force the target domain and the source domain to have the same distribution, which helps to improve the stability of adversarial training. Correspondingly, a timestamp mapping-based vibration signal screening method is proposed to improve data preprocessing efficiency for fault diagnosis of industrial robots. Extensive experimental results show that the accuracy of DPADA is superior to convolutional neural network (CNN) and conditional domain-adversarial network (CDAN)-based methods. A comparison is further performed on transfer tasks in three classical transfer scenes of industrial robots.