APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN-PROCESS FAULT-DIAGNOSIS

Fault diagnosis has been studied very actively during recent years. Estimation methods, rule-base reasoning and pattern recognition techniques are the most common methods used to solve problems. In recent years artificial neural networks have been used successfully in pattern recognition tasks and their suitability for fault diagnosis problems has also been demonstrated. However, the results presented in the literature usually consider very simple example situations. In this paper a realistic heat exchanger-continuous stirred tank reactor system is studied as a test case. The system with 14 noisy measurements and 10 fault situations is studied. The arrangement of different fault categories is visualized by the principal component analysis. The fault detection and diagnosis is based on the classification of process measurements and the classification is carried out using neural networks.