DIAGNOSTICS OF ROLLING BEARINGS FOR AUXILIARY ELECTROMOTORS OF ELECTRIC LOCOMOTIVE USING PARAMETRIC MODEL AND ENVELOPE SPECTRUM
Goal: increase of efficiency for diagnostics of rolling bearing faults using an autoregressive model to calculate AR coefficients and further application of pre-whitening AR filter and envelope spectra to extract weak faults signs.
Method of doing research: diagnostics of rolling bearing faults involves signal acquisition technique and application of the AR model for better analysis of short duration signal properties and impulses. The Akaike information criterion is used to ensure optimum adaptation of AR coefficients to a fault bearing. The AR coefficients are defined with Yule-Walker equitation. The advantages of pre-whitening AR filter are presented due to the low efficiency regarding the power spectral density of the parametric model. The experimental study of vibration characteristics of the auxiliary electromotor body of electric locomotive defines the frequency band 5,5 — 7 kHz with the rolling bearing vibration, and this frequency band can be used for further extraction the envelope spectra.
Value: the research shows a capability of the pre-whitening AR model to store valuable information not only about different faults concerning outer, inner race, rollers of bearings but also about the technical condition of the cage, the signs of which are displayed on the envelope spectra directly after the AR filter.
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