基于EMD与BP神经网络的齿轮故障诊断

Gear Fault Diagnosis Based on EMD and BP Neural Network

针对基于传统BP神经网络的齿轮故障诊断方法存在收敛速度慢,误差较大等问题,提出经验模式分解(EMD)与BP神经网络相结合的齿轮故障诊断方法。首先简述经验模式分解和BP神经网络的基本原理,然后采用EMD方法提取齿轮时域信号中的各个IMF分量,计算IMF分量中故障信号能量特征参数,将这些能量特征参数作为BP神经网络输入参数进行故障诊断。在齿传动故障实验台上采集足够的样本数据进行实验研究。结果表明:与传统的BP神经网络相比,可将训练误差从0.01降低至0.001左右。此外,训练迭代次数可减小至10次以内。

Aiming at the problems of slow convergence speed and large error in gear fault diagnosis based on traditional BP neural network,a gear fault diagnosis method combining EMD and BP neural network is proposed.Firstly,the basic principles of EMD and BP neural network are introduced.Then,the IMF components of gear time domain signals are extracted by EMD method,the energy characteristic parameters of fault signals in IMF components are calculated,and these energy characteristic parameters are used as input parameters of BP neural network for fault diagnosis.Sufficient sample data are collected on the gear transmission failure test-bed for experimental study.The results show that compared with the traditional BP neural network,the training error can be reduced from 0.01 to 0.001.In addition,the number of training iterations can be reduced to less than 10.