LAUSR

Abstract The mechanism of classification of the RNNs was revealed in this study. The benchmark of the Tennessee Eastman process was used to demonstrate the performance of the RNN-based fault diagnosis model. This study investigated the fault diagnosis performance of the entire and early detections for complicated chemical processes using the time-series recurrent neural networks (RNNs). The investigation included various layers and neuron nodes in RNNs using lean and rich training datasets and compared these RNNs with the artificial neural networks (ANNs). The results showed that the RNNs had better classification accuracies than the ANNs regardless of using lean or rich training datasets. The general classification mechanism was a priori classification that used normal operating data as the center so that it was incapable of separating the fault types having similar features of the normal operating data. RNNs drove the normal operating data out of the center and created the even spatial distribution of fault types, leading that RNNs were effective in classifying the fault types with subtle features when there is sufficient data.