A Fault Diagnosis Intelligent Algorithm Based on Improved BP Neural Network

Abstract

The fault diagnosis intelligent algorithm makes full use of the associative memory and pattern recognition function of the neural network to compare the abnormal value of various parameters of the engine fault with the reference value of the known fault mode, which can shorten the fault diagnosis time and improve the diagnosis efficiency. BP neural network model as one of the most widely used neural network models in the world is of significance to solve nonlinear complex problems. Of course, there are also some deficiencies in it, such as long training time and ease to trap into local minimum. This paper utilized the global search advantage of genetic algorithm to optimize the optimal weight and threshold value of BP neural network. Furthermore, an improved BP neural network was put forward, which is greatly improved in stability, generalization and convergence rate. Taking fault diagnosis of automobile engine as an example, a simulation experiment was carried out on the established model. The research results indicate that improved neural network model owns a higher accuracy than pure GA model or BP neural network model (with an average accuracy improved by 19.04% than traditional model), and its effect is satisfactory.