A sequential Monte Carlo filtering approach to fault detection and isolation in nonlinear systems

Much of the development in fault detection schemes have relied on the system being Linear and the noise and disturbances being Gaussian. In such cases, optimal filtering ideas based on Kalman filtering is utilised in estimation followed by a residual analysis for which whiteness tests are typically carried out. Linearised approximations have been used in the nonlinear systems case. However, linearisation techniques, being approximate, tend to suffer from poor detection or high false alarm rates. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distribution of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution. We compare the fault detection performance with that using the extended Kalman filtering and investigate the isolation performance on a nonlinear system.