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This paper presents a Fault Detection and Isolation (FDI) method for stochastic nonlinear dynamic systems. Our contribution consists of showing another method of tackling the problem of the physical origin diagnosis of faults by combining the technique based on the innovations and the technique using the multiple Kalman filters for a nonlinear dynamic system strongly non-stationary. The usefulness of this combination is the implementation of all the faults dynamics if the decision threshold on the standardized innovation exceeds a fixed value. In the other case, one filter is enough to estimate the process state. An algorithm is described and applied to a perfectly stirred chemical reactor functioning in a semi-batch mode. In this paper, the chemical reaction used is an oxido reduction one, the oxidation of sodium thiosulfate by hydrogen peroxide.

On-line Fault Detection and Isolation for State Space Models Using a Combination of Extended Kalman Filters and the Standardized Innovation Approach