Fault Diagnosis of Industrial Robots Based on Multi-sensor Information Fusion and 1D Convolutional Neural Network

Abstract

The performance of the industrial robot servo system (IRSS) depends on two factors, one is the control algorithm and mechanical processing accuracy during system design, and the other is maintenance during system operation. Based on the strategy of condition-based maintenance, the long-term stable high-performance operation of the industrial robot servo system can be maintained. In order to improve the performance of the servo system through the predictive maintenance of industrial robots, we need to monitor the operating state of the equipment during its operation and use intelligent algorithms to identify the operating state. The fault diagnosis of industrial robots represented by bearing fault diagnosis plays a crucial role in the optimization of IRSS. In the early stages of faults, online and accurate diagnosis can achieve predictive maintenance and improve the performance of IRSS. In this paper, a new multi-sensor information fusion technology is proposed, which uses the signals of multiple sensors as the input of a one-dimensional (1D) convolutional neural network (CNN), and implements a fault classification method through an improved CNN. This method is verified on the public data set of Case Western Reserve University and the IMS bearing database of the University of Cincinnati. Compared with the traditional 1D or 2D CNN and other fault classification methods, the model is simplified and can be used more Less data and simpler calculation complexity achieve higher fault classification accuracy.