Identification of Axle-Box Bearing Faults of Freight Cars Based on Minimum Entropy Deconvolution and Squared Envelope Spectra

Abstract

Purpose: To improve the performance of vibration spectral methods in identification of bearing element faults of freight car axle-boxes.Approach: An algorithm for simulating the expected vibration signal of outer race bearing was implemented. The autoregressive filter and minimum empirical deconvolution method was applied to identify the ball pass outer-race fault frequency and its harmonics on the envelope spectra and squared envelope spectra which were extracted in the proper frequency band.Results: The simulated vibration signal of a faulty bearing shows suitability of the autoregressive filter and minimum empirical deconvolution method, envelope and squared envelope spectra for outer race fault identification. There were observed a lower amount of features and their impulse sharpness in outer race faults in the bearing test rig than on the spectra in the wheelset test rig.Conclusions: The deterministic components are removed in the residual signal after using the AR filter and the impulse and noise components that decrease the kurtosis value remain in it. The MED technique additionally enhances the magnitude of increased BPFO components after using the AR filter and, together with it, provides satisfied performance and increases the efficiency of vibration diagnostics.