Early fault signal processing for an EMU rolling bearing based on acoustic emission
AbstractThe rolling bearing is an important part between the wheel axle and the bogie frame of Electric Multiple Units (EMU), and the running state directly affects the vehicle stability. Therefore, early fault diagnosis is the technical guarantee of the safe operation of the EMU. At present, as temperature and vibration detection technology cannot identify early faults, this paper proposes a signal processing method based on acoustic emissions. LMS adaptive filter technology is used to filter the acoustic emission original waveform stream to improve the signal-to-noise ratio of the fault signal. The envelope demodulation method is used to extract the low frequency fault signal by means of the cubic spline interpolation Method; then, the power spectrum of the low frequency fault signal is analysed. Taking a natural small defect in an EMU bearing as an example, the acoustic emission signal is analysed. The results show that the proposed method could identify early faults in rolling bearings under different operating EUM speeds.
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