基于粒子群优化神经网络的水轮机振动故障诊断

为了提高水轮机振动故障诊断正判率，提出粒子群算法优化BP神经网络的水轮机振动故障诊断方法，即把通过特征提取获得的机组故障特征量作为神经网络的输入，然后利用训练好的粒子群算法优化后的神经网络进行水轮机振动故障类型诊断。诊断结果表明，该方法具有良好的分类效果，比BP神经网络诊断模型诊断精度高。

Hydraulic Turbines Vibration Fault Diagnosis by Neural Network Based on Particle Swarm Optimization

In order to improve the correct rate, this paper put forward a method of the vibration faults diagnosis of hydraulic turbines by neural network based on particle swarm optimization (PSO). Some fault characteristics through the feature extraction were selected as the inputs of neural network for training, and then the fault diagnosis was accomplished via the trained and optimized neural network. The experimental results show that this method gains good classification results, and it has a more rapid convergence speed and higher diagnosis precision than BP neural network model.