Learning features from High Speed Train vibration signals with Deep Belief Networks

Feature extraction is one of key steps in fault diagnosis for High Speed Train (HST). In this work, we present a method that can automatically extract high-level features from HST vibration signals and recognize the faults. The method is composed of a Deep Belief Network (DBN) on Fast Fourier Transform (FFT) of vibration signals. DBNs can be trained greedily, layer by layer, using a model referred to as a Restricted Boltzmann Machine (RBM). The real data sets and simulation data sets of HST vibration signals are selected in experiments. First, the vibration signals are preprocessed by FFT. Then, the FFT coefficient-vectors are used to set the states of the visible units of DBNs. Finally, n label units are connected to the "top" layer of the DBNs to identify different faults. The experimental results show that the method may learn useful high-level features from vibration signals and diagnose the different faults of HST.