Fault Diagnosis of Industrial Robots Based on Phase Difference Correction Method

Abstract

Aiming at the characteristics of the fault spectrum of industrial robots, a new phase difference correction method is proposed on the basis of Fourier transform, which combines autocorrelation technology and windowing technology to convert the original signal into a discrete spectrum with fault characteristics, which effectively improves the accuracy of fault spectrum correction and provides important help for robot fault diagnosis. Simulation analysis and example verification show that the new algorithm is quite effective in the extraction of industrial robot fault features, and the algorithm still has a smaller relative error than the traditional algorithm under noise conditions, with high estimation accuracy and strong compatibility and robustness. The algorithm not only has high theoretical value in pattern recognition, but also has great practical significance in engineering fields such as robot diagnosis.