Abstract
Rolling element bearings are crucial components in rotating machinery, and avoiding unexpected breakdowns using fault detection methods is an increased demand in industry today. Variable speed conditions render a challenge for vibration-based fault diagnosis due to the non-stationary impact frequency. Computed order tracking transforms the vibration signal from time domain to the shaft-angle domain, allowing order analysis with the envelope spectrum. To enhance fault detection, the bearing resonance frequency region is isolated in the raw signal prior to order tracking. Identification of this region is not trivial but may be estimated using kurtosis-based methods reported in the literature. However, such methods may fail in the presence of relatively strong non-Gaussian noise. Cepstrum pre-whitening has also been proposed for this diagnosis challenge, however the noise floor may increase significantly from the normalization of the entire spectrum. In this paper, a new approach for identifying multiple resonance regions is proposed. The proposed method highlights all resonance frequencies in the signal by combining computed order tracking and cepstrum pre-whitening in a new way. Simulations and experimental results prove the validity of the method, and comparisons with two existing methods show the increase in effectiveness of the proposed method.
Published Version
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