Emulsifier fault diagnosis based on back propagation neural network optimized by particle swarm optimization

Abstract

The paper focuses on the fault of emulsifier during the production of emulsion explosive as the research object. Aiming at the conventional Back Propagation (BP) neural network has the defects of tardy convergence rate and undesirable optimal ability in vibration fault diagnosis of emulsifier a method of optimizing the BP neural network based on improved particle swarm optimization was presented. It can optimize initial weight and threshold of the BP neural network, and diagnose the fault of emulsifier. Instance simulation results show that the model of the BP neural network based on improved particle swarm optimization fault diagnosis has better classification effect and improves the fault diagnosis accuracy of the emulsifier.