An Optimal Weighted Spectral Difference Method for Faulty Vibration Separation in Flexible Joint Robot

Abstract

When a robot flexible joint faults, the fault component of the vibration signal is masked by the system dynamic response vibration and interferences. First, the dynamic response vibration estimated by the method combines a dynamic model and a convolutional neural network (CNN) error compensation model. The estimated dynamic response vibration is taken as the reference signal generated in the operation of healthy condition. In order to separate the fault components from the measured vibration in abnormal condition, an optimal weighted spectral difference method is proposed. The basic idea of the method is to find the optimal difference between the dynamic response vibration and the fault component through a convex optimization model. Finally, the experimental results on a flexible joint robot verify the effectiveness of the proposed method, and the fault components are extracted successfully from the measured vibration.