Simplification techniques for EKF computations in fault diagnosis: Model decomposition

The extended Kalman filter (EKF) is one of the most popular model-based techniques for fault detection and diagnosis. In this study, the suboptimal EKF technique is utilized to enhance computation efficiency without sacrificing diagnostic accuracy. In particular, three simple strategies are proposed to decompose the filter model according to the precedence order of the state/parameter estimation process. The computation load needed in fault identification can be reduced significantly by implementing all or part of these decomposed EKFs on-line. Extensive simulation results are also presented to demonstrate the effectiveness of these proposed techniques.