A new BRB based method to establish hidden failure prognosis model by using life data and monitoring observation

Abstract

It is important to predict the hidden failure of a complex engineering system. In the current methods for establishing the failure prognosis model, the qualitative knowledge and quantitative information (life data and monitoring observation) cannot be used effectively and simultaneously. In order to predict the hidden failure by using the qualitative knowledge, life data and monitoring observation, a new model for hidden failure prognosis is proposed on the basis of belief rule base (BRB). In the newly proposed model, there are some unknown parameters whose initial values are usually given by experts and may not be accuracy, which may lead to the inaccuracy prediction. In order to tune the parameters of the failure prognosis model according to the life data and monitoring observation, an optimal algorithm for training the parameters is further developed on the basis of maximum likelihood (ML) algorithm. The proposed model and optimal algorithm can operate together in an integrated manner to improve the precision of failure prognosis by using the qualitative knowledge and quantitative information effectively. A case study is examined to demonstrate the ability and potential applications of the newly proposed failure prognosis model.