基于量子粒子群优化的BP神经网络的汽轮机振动故障诊断研究

Fault Diagnosis of Steam Turbine Based on QPSO_BP

为了提高汽轮机诊断系统的诊断速度与精度,提出了将量子粒子群算法和BP神经网络相结合的故障诊断方法。用量子粒子群算法来训练网络的权值和阈值,再将优化后的权值和阈值代入BP网络,进行故障诊断。实例证明,它是一种高效,可靠的诊断方法。

To improving the diagnosed speed and accuracy of the steam turbine diagnose system,this paper proposes a method that combines QPSO with BP neural networks.QPSO was used to optimize the connection weights and thresholds of BP neural networks;then put them back into BP neural networks.And this algorithms was used for fault diagnose of steam turbine.It was certified as a efficient,reliable method of diagnosis in this example.