Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process

Abstract The paper deals with problems of fault detection of industrial processes using dynamic neural networks. The considered neural network has a feed-forward multi-layer structure and dynamic characteristics are obtained by using dynamic neuron models. Two optimisation problems are associated with neural networks. The first one is selection of a proper network structure which is solved by using information criteria such as the Akaike Information Criterion or the Final Prediction Error. In turn, the training of the network is performed by a stochastic approximation algorithm. The effectiveness of the proposed fault detection and isolation system is checked using real data recorded in Lublin Sugar Factory, Poland. Additionally, a comparison with alternative approaches is presented.

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