In the general stochastic nonlinear and
non-Gaussian systems, the sensor faults including biased and scaled readings
caused by sudden calibration errors have adverse effect on the precise monitor
and stable control of the system. To deal with this problem, a novel diagnosis
and identification method is proposed. An adaptive particle filter is developed
to calculate the difference between the measurements and the particle filter
estimates, then the type and magnitude of sensor faults are determined through
with maximum likelihood estimation, thus the fast and precise detection of
sensor fault is realized, and the adverse effect caused by the faults can be
compensated. Some simulations are carried out on a boiler model, and the results
validate the effectiveness of the proposed method.
