Condition identification and health assessment of electromechanical equipment based on similarity hierarchical clustering

In the context of intelligent manufacturing and industrial automation, this paper addresses the problem of difficulty in establishing a fixed baseline for health assessment due to changes in operating conditions and environmental variations during the operation of electromechanical equipment. A hierarchical clustering method based on Dynamic Time Warping (DTW) distance is proposed. This method calculates the DTW distances between pairwise signals and constructs a weighted matrix using key feature parameters for clustering, enabling effective categorization of the equipment's operating conditions. By constructing health baselines under different operating conditions, this approach facilitates the classification and evaluation of various parameter features of the electromechanical equipment. The overall health status of the equipment is obtained through weighting, and the method has been validated on an industrial robot, with results demonstrating its strong practicality.