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.