Aiming at the low accuracy and efficiency in fault diagnosis of magnetic resonance imaging equipment,a fault diagnosis method based on particle swarm optimization algorithm and back propagation neural network combined with Dempster-Schafer evidence theory is proposed.This method optimizes the parameters of back propagation neural network by particle swarm algorithm and fuses multi-sensor data by combining Dempster-Schafer evidence theory.Experimental results show that the average detection accuracy of the proposed model for 10 types of faults is 96.2%,the single sample detection time is 17.5 ms,and the accuracy rate reaches 100%in the detection of 10 types of faults.
关键词
粒子群优化算法/反向传播神经网络/磁共振成像设备/故障诊断/邓普斯特-谢弗证据理论
Key words
PSO algorithm/BP neural network/magnetic resonance imaging equipment/fault diagnosis/Demp-ster-Shafer evidence theory
维普
万方数据