An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter

The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The morphologic analysis of the locomotive bearing vibration signal, which are always contaminated by certain types of noise, is very important standard for mechanical condition diagnosis of the locomotive bearing and mechanical failure phenomenon. In this paper a novel vibration signal enhancement method based on empirical mode decomposition (EMD) and adaptive filtering is proposed to filter out Gaussian noise contained in raw vibration signal. The reference signal of the adaptive filter is produced by selective reconstruction of the decomposition results of EMD. Real vibration signals from the locomotive bearing are used to validate the performance of the proposed method. Conventional EMD and adaptive EMD are tested to compare the filtering performance. The results of simulation show that the vibration signal can be significantly enhanced by using the proposed method. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearing with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.