Generalized SSPRT for Fault Identification and Estimation of Linear Dynamic Systems Based on Multiple Model Algorithm

Abstract

The generalized Shiryayev sequential probability ratio test (SSPRT) is applied to linear dynamic systems for singlefault isolation and estimation. The algorithm turns out to be the multiple model (MM) algorithm considering all thepossible model trajectories. In real application, this algorithm must be approximated due to its increasing computationcomplexity and the unknown parameters of the fault severeness. The Gaussian mixture reduction is employed toaddress the problem of computation complexity. The unknown parameters are estimated in real time by modelaugmentation based on maximum likelihood estimation (MLE) or expectation. Hence, the system state estimation,fault identification and estimation can be fulfilled simultaneously by a multiple model algorithm incorporating these twotechniques. The performance of the proposed algorithm is demonstrated by Monte Carlo simulation. Although ouralgorithm is developed under the assumption of single fault, it can be generalized to deal with the case of (infrequent)sequential multiple faults. The case of simultaneous faults is more complicated and will be considered in future work.