小波包-神经网络在摆式列车倾摆控制系统故障诊断中的应用

针对摆式列车倾摆控制系统故障的特点，研究了神经网络结合小波包分析进行故障诊断的方法，采用小波包分解和信号重构的方法，将在摆式列车试验台上采集到的振动信号分解到不同的频带以提取有关的故障信息，并将振动信号各频带内的能量特征作为训练样本输入前向神经网络，用优于改进梯度下降法的Levenberg—Marquardt优化方法对网络进行训练，对倾摆控制系统的常见故障进行识别和诊断。实践表明，该方法对摆式列车倾摆系统故障的诊断是可靠的。

Fault diagnosis using wavelet packet and neural network in tilting control system of tilting train

With the view of fault characteristics of tilting control system,this paper put forward a fault diagnosis approach employing a combination of wavelet packet and neural network.The method using wavelet packet decomposition and signal reconstruction was proposed to extract fault information from vibration signal obtained from testing jig of tilting train. By analyzing the energy of signal in full spectrum bands,the symptom that representes fault was inputted to a feed forward neural network trained by Levenberg-Marquardt optimization,which progress was very fast comparing with the improved gradient descent algorithm.The trained feed forward neural network can report the typical faults of tilting control system.Trial and research show that the method is practicable for fault diagnosis in tilting control system of reality tilting train.4 tabs,1 fig,8 refs.