An optimized long short-term memory network based fault diagnosis model for chemical processes

Abstract With the development of the chemical industry, fault diagnosis of chemical processes has become a challenging problem because of the high-dimensional data and complex time correlation caused by the more complex chemical processes and increasing number of equipment. However, the ordinary feedforward neural network cannot solve these problems very well. Therefore, this paper proposes a fault diagnosis model based on the optimized long short-term memory (LSTM) network. Since the number of hidden layer nodes in the LSTM network has a great influence on the diagnosis result, the link of determining the optimal number of hidden layer nodes by the iterative method based on the LSTM network is added. Then the LSTM is optimized to get higher chemical process fault diagnosis accuracy. Finally, through the simulation experiment of the Tennessee Eastman (TE) chemical process, the results verify that the optimized LSTM network has better performance in chemical process fault diagnosis than the BP neural network, the multi-layer perceptron method and the original LSTM network.