Crack detection in freight railway axles using Power Spectral Density and Empirical Mode Decomposition Techniques

Abstract

One of the most critical systems in the operation of a train is the wheelset. This mechanical element is composed of the wheels and the axle. The wheelset must transmit the weight and dynamic forces of the railway vehicle to the track, so it is under the action of high and repetitive loads. Hence, the failure of this element could result in a catastrophic accident. This work studies the vibratory behavior of the running gear system of a freight train with a defect induced in the axle. The severity of the defect will depend on the size of the defect. Vibration signals are taken from sensors located in the axle box and will be processed using the Empirical Mode Decomposition (EMD) technique. The EMD technique decomposes the temporal signal into some elementary intrinsic mode functions (IMF), which are the result of progressive envelopes of the temporal signal and that work as bandpass filters. The spectral power of each IMF reflects the frequency behavior of the vibratory signal for the frequency band associated with each IMF. The evolution of these IMF spectral powers will be studied for each defect level, so we can determine if this evolution can be used as an indicator of the condition of the wheelset.