FAULT DIAGNOSIS METHOD OF SUBMERSIBLE SEWAGE PUMP BASED ON IMPROVED HOPFIELD NEURAL NETWORK

Abstract

In order to accurately diagnose the fault of submersible sewage pump, an improved Hopfield neural network（HNN） fault diagnosis method was proposed. BP neural network was used for coding operation to overcome the coding defects of HNN neural network. The connection weights of HNN neural network were optimized by particle swarm optimization（PSO） algorithm to improve the global convergence ability of the improved neural network, and the improved HNN neural network model was obtained. Based on the field experiments, the vibration signal feature vector of the submersible sewage pump under fault operation was obtained. Then the feature vector was used as sample data to train the improved neural network, and the fault types of the submersible sewage pump were diagnosed. The results show that the improved HNN neural network has better global convergence ability, and the typical fault diagnostic accuracy of the submersible sewage pump is more than 90%, which can realize the accurate diagnosis of the fault during the operation of the submersible sewage pump.

Keywords

• Submersible sewage pump;Improved Hopfield neural network;PSO algorithm;Fault diagnosis;Vibration signal