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

The generalized Shiryayev sequential probability ratio test (SSPRT) is applied to linear dynamic systems for single fault isolation and estimation. The algorithm turns out to be the multiple model (MM) algorithm considering all the possible model trajectories. In real application, this algorithm must be approximated due to its increasing computation complexity and the unknown parameters of the fault severeness. The Gaussian mixture reduction is employed to address the problem of computation complexity. The unknown parameters are estimated in real time by model augmentation 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 two techniques. The performance of the proposed algorithm is demonstrated by Monte Carlo simulation. Although our algorithm 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.