A parameterized Doppler distorted matching model for periodic fault identification in locomotive bearing

Abstract

The locomotive bearings support the whole weight of the train under the high speed and its continuous running is a key factor of the train safety. The collected Doppler distorted signal greatly increases the difficulties in detecting the whelmed fault information. To overcome this disadvantage, a novel Doppler distorted correlation matching model using the single side Laplace wavelet and acoustic theories is constructed to recognize the bearing fault-related impulse intervals. The parameterized Doppler distorted model is assessed by the correlation coefficient with the bearing fault signal in waveform. The optimal Doppler distorted model, that is the Doppler distorted correlation matching model which matches the fault component in the acoustic signal could be used for identifying the fault impulse interval. Then, the bearing fault can be successfully detected from the parameters of the Doppler distorted correlation matching model due to its maximum similarity to the real signal. Except for the simulation study, the proposed model is also employed to match the fault components in some real acoustic bearing fault signals. The recognized fault impulse period in the Doppler distorted correlation matching model’s initial model keep in good accordance with the correct fault impacts, which represent the locomotive bearing fault characteristics.