## 1 Introduction  
Electrical connectors are widely used in equipment systems and are of great importance. The contact condition of connectors significantly affect the reliability and the success rate of the equipment [1,2]. However, with the increasing service time and the influence of environmental stress, the using states of electrical connectors will be continuously deteriorated, and eventually the connectors failed. To avoid a catastrophic accident, it is necessary to assess the using state of the electrical connectors in good time, and predict the residual life of electrical connectors. Residual life prediction is an important part of Prognostics and Health Management (PHM) technology. At present, experts and scholars have carried out a lot of research on residual life prediction for various connectors, including relays, circuit breakers, Contactors, electrical connectors, and so on. Because of the different structures and functions of connectors, the residual life prediction methods for connectors are different, as well as the accuracy of these prediction methods. Generally, the accuracy of the prediction methods heavily depends upon extracted life features. With the rapid development of electronic technology, many new features of life prediction have emerged [3, 4, 5, 6]. For example, in the study of the prediction of relay and circuit breakers, the overrange time, the effective contact distance, the surface roughness, the arc time, the contact quality loss and the arc voltage spectrum are often extracted for residual life prediction. Unlike relays and circuit breakers, the plug and socket of electrical connectors are pressed together; this mechanical structure made the life prediction features difficult to extract. The new features can, however, directly reflect the damage and degradation of the electrical connectors but the new prediction features are not suitable for on-line monitoring, and are also difficult to extract. Therefore, more suitable features need to be studied for the residual life prediction of electrical connectors. Considering practicality and generalization, most of the research on the life prediction of electrical connectors is still based on contact resistance data. Recently, many new prediction methods for contact resistance have been proposed [7; 8]. [PERSON] et al. proposed a FEM-based method to estimate the contact resistance of two mating metallic rough surfaces. [PERSON] et al. developed a genetic algorithm (GA) approach to predict the constriction resistance. Besides, much engineering and experimental data show that contact resistance is nonlinear through the whole life cycle of the electrical connector [1; 9; 10] and only when the contact resistance is approaching permanent failure does the contact resistance quickly rise, showing the obvious failure feature. The contact resistance remains low in the early and middle stages of degradation. Therefore, it is difficult to ensure the accuracy of life prediction in the early and middle stages of the degradation. With the aging of electrical connectors, intermittent failure often occurs during the operation period. An intermittent failure is defined as a failure of an item for a limited period of time, following which, the item recovers its ability to perform its tasks without being subjected to any external corrective action. Once the first intermittent failure occurs, the intermittent failure will continue to occur during the residual life of electrical connectors and the \"severity\" of the intermittent failures will grow with increasing service time. By obtaining the \"severity\" characteristics of intermittent failures, guidance can be provided for research, such as using states estimation, residual life prediction, maintenance plan, false alarm suppression and so on. Therefore, much research is focused on the characterization of intermittent failure \"severity.\" By defining the pseudo cycle of intermittent failure, [PERSON] et al. investigated the dynamics of intermittent failures [11].