Abstract:Rolling bearing is one of the most common mechanical equipment, which performance degradation assessment is the base to realize condition-based maintenance. In recent years, the rolling bearing monitoring technology has made some achievements, but to find a capable monitoring system, which has the advantages of high reliability, high efficiency and early warning of the bearing fault is a major challenge. A equipment performance degradation assessment method was presented based on time encode signal processing and recognition (TESPAR), which was used in the experiment of rolling bearing on different fault degree and full life test. The results show that the TESPAR analysis can distinguish the different degree of failure accurately, and is sensitive to the change of bearing fault.