A model for online failure prognosis subject to two failure modes based on belief rule base and semi-quantitative information

As one of most important aspects in condition-based maintenance (CBM), failure prognosis has attracted an increasing attention with the growing demand for higher operational efficiency and safety in complex engineering systems. Currently there are no effective methods for predicting the failure of a system in real-time by using both expert knowledge and quantitative information (i.e., semi-quantitative information) when degradation failure and shock failure are dependent and competitive. Since belief rule base (BRB) can model the complex system when semi-quantitative information is available, this paper focuses on developing a new BRB based method for online failure prognosis that can deal with this problem. Although it is difficult to obtain accurate and complete quantitative information, some expert knowledge can be collected and represented by a BRB which is an expert system essentially. As such, a new BRB based prognosis model is proposed to predict the system failure in real-time when two failure modes are dependent and competitive. Moreover, a recursive algorithm for online updating the parameters of the failure prognosis model is developed. Equipped with the recursive algorithm, the proposed prognosis model can predict the failure in real-time when two failure modes are dependent and competitive. An experimental case study is examined to demonstrate the implementation and potential applications of the proposed online failure prognosis method.