Fault Diagnosis for TE Process Using RBF Neural Network

Fault diagnosis of industrial process has long been a challenging issue owing to the industrial system that exhibits nonlinearity, coupled parameters and time-varying in the production process. This paper presents a novel dynamic fault diagnosis model (AUKF-RBF) based on radial basis function (RBF) neural network for Tennessee Eastman (TE) industrial process. In order to effectively reflect the dynamic features of industrial system, a dynamic fault diagnosis model is established based on UKF and RBF neural network. In particular, UKF is used to optimize the weights, the center, and the width of the hidden layer nodes of RBF. Furthermore, to reduce the effect of the inappropriate initial filter parameters in UKF, an adaptive factor
$\delta _{k}$
is developed to tune the covariance matrix adaptively. Finally, the proposed fault diagnosis algorithm is applied to TE benchmark industrial process. Experimental results show the effectiveness of the proposed fault diagnosis method.