Fault Diagnosis for TE Process Using RBF Neural Network

Xin Liu,Hai He

doi:10.1109/access.2021.3107360

Abstract

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.

Highlights

M ODERN industrial systems are very complicated due to the structure, production flow, and automation degree [1]
A dynamic fault diagnosis model is established based on Unscented Kalman filter (UKF) and radial basis function (RBF) neural network, where UKF is used to optimize the weights, the center, and the width of the hidden layer nodes of RBF
It is obvious that the accuracy of UKF-RBF and AUKF-RBF with dynamic molding is significantly higher than Back propagation (BP) and RBF, especially all the output errors of AUKF-RBF are less than 0.5

Summary

INTRODUCTION

M ODERN industrial systems are very complicated due to the structure, production flow, and automation degree [1]. Liu et al [17] proposed an adaptive RBF neural network to approximate nonlinear fault function. In [19], a combined with particle swarm optimization (PSO) and RBF neural network method was proposed to diagnose the nonlinear prediction model. Ke et al [20] proposed a selfadaptive RBF neural network method for power transformer fault diagnosis. UKF has been used to establish dynamic evolution modeling by optimizing the neural network model due to its strong nonlinear tracking ability [25, 26]. A novel fault diagnosis approach based on adaptive UKF and RBF neural network is proposed for fault x1 w(1). A dynamic fault diagnosis model is established based on UKF and RBF neural network, where UKF is used to optimize the weights, the center, and the width of the hidden layer nodes of RBF. In the RBF, the linearly inseparable problem in low-dimensional space is transformed to the linearly separable problem in highdimensional space, effectively overcoming the problems of local minima and slower convergence speed in a BP neural network [33]

UNSCENTED KALMAN FILTER

EXPERIMENTS AND DISCUSSION

PERFORMANCE OF FAULT DIAGNOSIS

Findings

CONCLUSION