Fault diagnosis of industrial process using attention mechanism with 3DCNN-LSTM

Abstract

Although architectures such as recurrent neural networks (RNN) perform well in time series prediction, there is still challenges for existing deep learning methods in extracting comprehensive spatio-temporal features. This paper proposes a fault diagnosis method (3D-ALCNN) using a three-dimensional convolutional neural network (3DCNN), a long-short-term memory network (LSTM) and an attentional mechanism to enhance the correlation perception among important features. The 3DCNN and LSTM are firstly used to extract temporal features and a data stacking method to adapt the one-dimensional data to the input requirements of 3DCNN is adopted. Then, the correlation between different features is identified through the attention mechanism and the neural network is guided to focus on more important features. Eventually, the model output is fed into a classifier for fault classification. Simulation experiments on the industrial coke furnace show that the 3D-ALCNN model outperforms existing methods in terms of fault diagnosis accuracy and efficiency, especially when dealing with chemical data with complex time dependencies.