Ae. Farell et Sd. Roat, FRAMEWORK FOR ENHANCING FAULT-DIAGNOSIS CAPABILITIES OF ARTIFICIAL NEURAL NETWORKS, Computers & chemical engineering, 18(7), 1994, pp. 613-635
Neural networks have demonstrated excellent performance in facilitatin
g automatic fault detection and diagnosis in many engineering applicat
ions. Their primary advantage over model-based and knowledge-based exp
ert systems is that they require very little development time or exper
tise. However, neural networks perform only as robustly as the data fr
om which they are trained. Therefore, understanding the content and li
mitations of process data used to train the network is crucial. This p
aper presents neural networks as part of a fault recognition framework
for diagnosing process inefficiencies. In this framework, incorporati
ng a small amount of process knowledge helps minimize data limitations
and maximize the neural network's performance. Data preprocessing and
filtering lead to significant improvement in recognition performance
and markedly reduced training time. In addition, the framework allows
detection of the ''unknown'' class. The fault recognition framework wi
ll be demonstrated via a simulated continuous stirred tank reactor sys
tem which operates under realistic disturbances and noisy measurements
.