Recurrent neural network based decision support system

TL;DR

This paper presents a fault diagnostic decision support system using recurrent neural networks and PCA for noise filtering, applied to nuclear reactor data, achieving accurate fault predictions.

Contribution

It introduces a novel combination of PCA and recurrent neural networks for fault diagnosis in complex systems, validated on nuclear reactor data.

Findings

Radial basis recurrent network accurately predicts fault location and size.

PCA effectively filters noise in the diagnostic process.

The system demonstrates high diagnostic accuracy across fault scenarios.

Abstract

Decision Support Systems (DSS) in complex installations play a crucial role in assisting operators in decision making during abnormal transients and process disturbances, by actively displaying the status of the system and recording events, time of occurrence and suggesting relevant actions. The complexity and dynamics of complex systems require a careful selection of suitable neural network architecture, so as to improve diagnostic accuracy. In this work, we present a technique to develop a fault diagnostic decision support using recurrent neural network and Principal Component Analysis (PCA). We utilized the PCA method for noise filtering in the pre-diagnostic stage, and evaluate the predictive capability of radial basis recurrent network on a representative data derived from the simulation of a pressurized nuclear reactor. The process was validated using data from different fault scenarios, and the fault signatures were used as the input. The predictive outputs required are the location and sizes of the faults. The result shows that the radial basis network gives accurate predictions. Selected hyperparameters and diagnostic results are also presented in this paper.