Model-based Online Fault Detection and Diagnosis (fdd) Strategy for a Chemical Reactor
نویسنده
چکیده
This paper presents a Fault Detection and Diagnosis (FDD) method for stochastic nonlinear dynamic systems. Our contribution consists to show an another way 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 nonstationary. The usefulness of this combination is the implementation of all the fault dynamics models 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. This chemical reaction is a very exothermic system already used for a thermal runway analysis of chemical reactors.
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