Fault prognosis based on evolving belief-rule-base system
基于进化信度规则库的故障预测(英文)

Recently, a sequential adaptive learning algorithm has been developed for online constructing belief-rulebased (BRB) system. This algorithm is based on the assumption that the sample density function of the inputs to BRB system obeys the uniform distribution. However, in practice, the sample density function is not always available and is difficult to be determined; this really limits the applicability of the above method. As such, it is desired to develop an improved algorithm without requiring the sample density function. In this paper, on the basis of the sequential adaptive learning algorithm, we develop an improved evolving BRB learning algorithm based on the belief-incomplete criterion. Compared with the current algorithms, a belief rule can be automatically added into the BRB or pruned from the BRB without the need of the sample density function. In addition, our algorithm inherits the features of the BRB, in which only partial input and output information are required. Based on the improved algorithm, a fault prognosis method is presented. In order to verify the effectiveness of our algorithm, a practical case study for gyroscope fault prognosis is studied and examined to demonstrate how our algorithm can be implemented.