Prognostics and health management (PHM) technology, in the modern complex equipment, high reliability, and high-security requirements, is a new technology concept to achieve condition-based maintenance. One of the research directions of PHM technology is to use the information contained in the system status monitoring data to evaluate, analyze and forecast the health and development of the equipment. Aiming at the problem of recessive pattern mining based on state monitoring data, a P-D-H clustering method is proposed to realize the mining of degradation pattern. First, the pattern of the degraded trajectory time series formed by the state monitoring data is represented by the piecewise aggregate approximation method. Then, the dynamic time warping distance is used as the similarity measure of the pattern sequence. Finally, the hierarchical clustering method is used to achieve the regression model clustering. In this way, the wear data of the rolling bearing wear condition is excavated and the effectiveness of the method is verified. The model clustering method based on the complex system state monitoring data can effectively realize the mining of the system health degradation pattern. The result of pattern mining can lay a good foundation for the system health forecasting.