Residual likelihood ratio test for fault diagnosis based on cost reference particle filter

The existing problems of common methods in fault diagnosis are briefly analyzed. In view of the adverse effect of external disturbance and the requirement of the successive implementation, by the triple integration of the cost reference particle filter, the interacting multiple model and the sequential probability ratio test, a novel residual likelihood ratio test algorithm based on cost reference particle filter for fault diagnosis is proposed. First, the cost reference particle filter is used to substitute the suboptimal filter in interacting multiple models, and the input interaction step and the output step are simplified. Then, the residual information is introduced into the sequential probability ratio test frame to construct an online residual likelihood ratio test method. The new algorithm realizes the efficient estimation for system state and the successive and reliable identification for system models. Computer simulation verifies the validity of this algorithm.