Fault prognosis of industrial robots in dynamic working regimes: Find degradation in variations

Abstract

Industrial robots are widely used in modern factories. Robot faults lead to the inevitable suspension of production lines. The prediction of robot failure can improve production capacity. However, it is challenging due to the variations of robots in dynamic working regimes. This paper presents a methodology of fault prognosis of industrial robots, including (1) a modeling approach of remaining useful life prediction using domain-generalization-adversarial long short-term memory to reduce the robot-to-robot variations, (2) an approach of two-stage health assessment based on principal component analysis-squared prediction error and p-chart that can reduce the disturbance of outliers in normal operations, and (3) a workflow containing feature extraction using wavelet packet decomposition, feature smoothing using exponential smoothing, feature normalization using z-score and feature selection using Pearson correlation coefficient. The case study of liquid crystal display transfer robots shows the methodology can effectively reduce the variations and improve the prediction of RUL.