Detection of multiple sensor faults in a palm oil fractionation plant using artificial neural network

Abstract

Given the universal approximation properties, simplicity as well its intrinsic analogy to the non-linear state space form, a recurrent Elman network is derived and applied as process predictor for fault detection in process plants. In this paper, a two-stage scheme integrating a neural Elman network dynamic predictor and a feedforward neural network fault classifier is proposed to overcome the problem of multiple sensor faults. The scheme was implemented to detect sensor failures in a palm oil fractionation process. To generate the required simulation data, Hysys.Plant dynamic process simulator was employed. The use of the output prediction error, between a neural network model and a non-linear dynamic process, as a residual for detecting sensor faults is analysed. A second neural network classifier is developed to detect the sensor faults from the residuals generated, and results are presented to demonstrate the satisfactory detection of two sensor faults achieved simultaneously using this scheme.