Train Axle Bearing Fault Diagnosis Based on Tunable Q-factor Wavelet Transform

Train axle bearings are one kind of key components in train vehicles and works under poor conditions which easily results in failure. Vibration signals generated by train bearings include rich information of its operating conditions. Signal processing plays an important part in discussing the vibration-based condition monitoring (CM) technique. In this paper, an efficient method Tunable Q-factor wavelet transform(TQWT) is proposed. Simulation and laboratory experiments are conducted to test the proposed method. Experimental results are verified the effectiveness of the proposed method.