Research Progress of Artificial Neural Network and Its Application in Fault Diagnosis of Chemical Industry

Many characteristics exhibited by artificial neural networks, such as nonlinearity, large scale, strong parallel processing ability, as well as robustness, fault tolerance, and strong self-learning ability, make it attractive for fault detection and diagnosis in complex systems. The relationship between the complex process, cumbersome process, and measurable process variable failure causes of chemical process is very complicated. Once a failure occurs, it will cause huge economic losses and casualties. The emergence of artificial neural network provides a new chemical fault diagnosis technology, which can carry out early and accurate fault detection and diagnosis for chemical process and equipment, so as to improve the efficiency and safety of production. This paper introduces the basic principle and development history of artificial neural network, as well as several typical artificial neural networks, such as back propagation algorithm (BP network), radial basis network (RBF network), and their application in chemical process fault diagnosis.