A Resonance-Identification-Guided Autogram for the Fault Diagnosis of Rolling Element Bearings

Rolling element bearings are key components for reducing friction and supporting rotors. Harsh working conditions contribute to the wear of bearings and consequent breakdown of machines, which leads to economic losses and even catastrophic accidents. Faulty impulses from bearings can excite resonance behavior in a system and produce modulation phenomena. Fault characteristics in modulated signals can be extracted using demodulation analysis methods, significantly improving the reliability and effectiveness of the fault diagnosis of rolling bearings. Optimal demodulation frequency band selection is a primary step for the demodulation-analysis-based fault diagnosis of bearing faults. To exploit the resonant modulation mechanism in the fault diagnosis of rolling element bearings, resonant frequencies identified through stochastic subspace identification are employed to guide the impulsive sparsity measures of an Autogram for bearing fault diagnosis, which combines physical modulation dynamics and data characteristics. The frequency band that not only matches the natural frequencies but also shows highly sparse impulsive characteristics is selected as the optimal demodulation frequency band for bearing fault diagnosis. The results of simulations and experimental data validate the advantages of the proposed method, which exploits physics-guided data processing for optimal demodulation frequency band determination.