Condition assessment is one of the most significant techniques of the equipment’s health management. Also, in PHM methodology cycle, which is a developed form of CBM, condition assessment is the most important step of this cycle. In this paper, the remaining useful life of the equipment is calculated using the combination of sensor information, determination of degradation state and forecasting the proposed health index. The combination of sensor information has been carried out using a new approach to determining the probabilities in the Dempster-Shafer combination rules and fuzzy c-means clustering method. Using the simulation and forecasting of extracted vibration-based health index by autoregressive Markov regime switching (ARMRS) method, final health state is determined and the remaining useful life (RUL) is estimated. In order to evaluate the model, sensor data provided by FEMTO-ST Institute have been used.

状态评估是设备健康管理中最重要的技术之一。同样，在PHM方法学周期（这是CBM的发展形式）中，条件评估是该周期最重要的步骤。在本文中，设备的剩余使用寿命是结合传感器信息，确定退化状态并预测所建议的健康指标来计算的。传感器信息的组合已经使用一种确定Dempster-Shafer组合规则和模糊c均值聚类方法中的概率的新方法进行了。使用通过自回归马尔可夫状态切换（ARMRS）方法对提取的基于振动的健康指数进行仿真和预测，可以确定最终的健康状态并估算剩余使用寿命（RUL）。