基于改进粒子群神经网络的提升机故障诊断

针对BP神经网络对提升机制动系统进行故障诊断存在着收敛速度慢和可靠性差等缺点,提出了一种基于粒子群神经网络的故障诊断方法.根据制动系统故障征兆与故障类型之间的非线性和耦合性,建立了提升机制动系统的故障诊断模型;采用改进的粒子群算法优化BP神经网络的连接权值和阈值,应用于制动系统的故障诊断,缩短了神经网络的训练时间,提高了故障诊断的精度.仿真结果表明该诊断方法具有故障诊断能力强和诊断效率高等特点.

Hoist fault diagnosis based on improved particle swarm neural network

In view of that BP neural network has a slow convergence problem and poor reliability for a hoist braking-system fault diagnosis, a neural network fault diagnosis method based on improved particle swarm optimization algorithm is proposed. Because of the nonlinear and coupling between the fault symptoms and the fault of a braking system, a hoist braking-system fault diagnosis model is established. Improved particle swarm optimization algorithm is applied to optimize the weights and thresholds of the network, and the optimized neu- ral network is used to diagnose braking system faults, which can shorten the neural network training time and improve the fault-diagnosis precision. The simulation results indicate that the diagnosis strategy has the characteristic of strong ability and high deficiency for diagnosis.