Chemical Process Fault Diagnosis Method Based on Deep Learning

Abstract

Abstract To address the problem that the traditional fault diagnosis method for chemical processes under big data relies too much on expert experience and fault features are difficult to distinguish, a deep learning-based fault diagnosis method is proposed, which combines convolutional neural network (CNN), long and short-term memory (LSTM) and attention mechanism (AM). In this method, the spatial sequence features of the input signal are extracted by the CNN adaptively, while the LSTM extracts the time-series features of the signal. Finally, the model performance is enhanced by introducing the attention mechanism and using the SoftMax layer as a classifier for fault diagnosis, so that the model can notice the important features of the faults with the interference of noise. Simulation validation of the method in this paper is performed using the TE chemical process data set, and it is demonstrated that the method can be used for chemical process fault diagnosis studies. Finally, compared with other fault diagnosis methods, the method is more accurate and has certain superiority.