Detecting sensor faults for a chemical reactor rig via adaptive neural network model

Authors:
Ding-Li Yu;Dingwen Yu
Affiliations:
Control Systems Research Group, School of Engineering, Liverpool John Moores University, Liverpool, UK;Dept. of Automation, Northeast University at Qinhuangdao, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

An adaptive neural network model based approach to sensor fault detection is proposed for multivariable chemical processes. The neural model is used to predict process output for multi-step ahead with the prediction error used as the residual, while the model is on-line updated to capture dynamics change. The recursive orthogonal least squires algorithm (ROLS) is used to adapt a radial basis function (RBF) model to reduce the effects of data ill conditioning. Two error indices are developed to stop the on-line updating of the neural model and its corresponding threshold is used to distinguish the fault effect from model uncertainty. The proposed approach is evaluated in a three-input three-output chemical reactor rig with three simulated sensor faults. The effectiveness of the method and the applicability of the method to real industrial processes are demonstrated.