Research on Industrial Monitoring Model Based on Neural Network Improvement

BP neural network is often used in industrial process monitoring and fault diagnosis. For complex industrial process with large amount of data, the training cycle of the network is long and the convergence speed is slow. In this paper, a new industrial process monitoring method is proposed, that is, principal component analysis is introduced to reduce the dimension of data samples. On this basis, the structure of neural network is determined according to TE process data variables, and then a new BP neural network monitoring model is established, and the feasibility of this method is proved by simulation experiments. The test results show that the model has the advantages of fast calculation speed, short training time and low error detection rate.