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Journal of applied research and technology

On-line version ISSN 2448-6736Print version ISSN 1665-6423

J. appl. res. technol vol.12 n.3 México Jun. 2014


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


Ji Zhang1, Yu Liu*2 and Xuguang Li3


1 Department of Computer, North China Electric Power University, Baoding, Hebei 071003, China.

2 Department of Electrical Engineering, University of New Orleans, New Orleans, LA 70148, USA.

3 Clinical Laboratory, 323 Hospital Xi'an, Shaanxi 710054, China.



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.

Keywords: Generalized SSPRT, state estimation, fault isolation and estimation, multiple model, Gaussian mixture reduction, model augmentation.





Research was supported by the Fundamental Research Funds for the Central Universities (China), project No. 12QN41.



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