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Chemical plants are complex largescale systems which need designing robust fault detection schemes to ensure high product quality, reliability and safety under different operating conditions. The present paper is concerned with a feasibility study of the application of the blackbox modeling method and Kullback Leibler divergence (KLD) to the fault detection in a distillation column process. A Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) polynomial model is ﬁrstly developed to estimate the nonlinear behavior of the plant. Furthermore, the KLD is applied to detect abnormal modes. The proposed FD method is implemented and validated experimentally using realistic faults of a distillation plant of laboratory scale. The experimental results clearly demonstrate the fact that proposed method is effective and gives early alarm to operators.
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