基于改进遗传算法的神经网络转向架轴承故障诊断

Fault Diagnosis of Bogie Bearing Based on Neural Network Optimized by Improved Genetic Algorithm

针对传统神经网络准确率较低且诊断时间较长的问题,提出一种基于改进遗传算法的神经网络故障诊断方法。通过简化遗传算法的编码方式,以最优适应度函数解码得到神经网络的权值、阈值,并以此建立基于改进遗传算法的神经网络转向架轴承故障诊断模型。为验证模型的有效性,与其他诊断模型进行了仿真计算对比。结果表明,所提出的基于改进遗传算法的神经网络转向架轴承故障诊断模型具有诊断耗时短、准确率高的优点。

Targeting the problems of low accuracy and long diagnosis time of conventional neural networks,a neural network fault diagnosis method based on improved genetic algorithm is proposed.By means of simplifying the genetic algorithm coding method,neural network weights and threshold values are acquired through decoding optimal fitness functions,thus a fault diagnosis model for bogie bearing based on neural network optimized by improved genetic algorithm is established.To verify the effectiveness of the model,simulation calculation and comparison are conducted with other diagnostic models.The results indicate that the neural network bogie bearing fault diagnosis model based on the improved genetic algorithm is timesaving and highly accurate.