Locomotive wheel bearings are prone to failure under harsh service conditions. Sparsity indexes which can evaluate the impulsive characteristics of bearing fault feature have been widely utilized in the signal processing methods. In recent years, sparsity indexes-based methods have been extensively criticized due to their high sensitivity to the random impact component. Although the feature extraction methods using the filtering framework can alleviate this issue, they still hardly achieve much in terms of the complex frequency spectrum, especially when the frequency bands of the fault and the interference information are mixed. To solve the problem, this paper proposes a robust impulse feature extraction method using dual-domain separation. Initially, the feature distinctions of the fault impulse and random impact component in the second-order amplitude-frequency (SAF) domain are explored in this paper. Subsequently, based on the distinctions, the dual-domain separation technique is performed in both the SAF and frequency domains, achieving the effective removal of random impact component. The performance of the methods including sparsity indexes-based gram, deconvolution, and decomposition methods is enhanced by using the proposed method. Finally, the strong robustness characteristics against random impact component are validated through simulation and experiment data from the locomotive wheel bearing.
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