A framework via impulses-oriented Gini index and extreme value distribution for rolling bearing dynamic fault alarm and identification

Rolling bearing dynamic fault alarm and identification is essential in condition-based maintenance and can prevent serious accidents. However, an integrated framework with dynamic adaptability and full interpretability is rarely reported for this issue based on sparsity measures and statistical properties. Therefore, a framework via impulses-oriented Gini index and extreme value distribution is developed in this paper. First, for attenuating normal-phase amplitude oscillations of the Gini index, periodical impulses-oriented Gini index (PIGI) and sparsity-smoothed periodical impulses-oriented Gini index (SPIGI) are defined successively through enhanced envelope impulses extraction to improve incipient fault sensitivity and robustness against interferences. Next, a dynamic alarm threshold setting strategy is built by fitting the available PIGIs using generalized extreme value distribution (GEVD). Further, a novel framework, without prior knowledge and complex signal processing algorithms, is established based on PIGI, SPIGI, and GEVD-based thresholds for dynamic fault alarm and identification. The verification results on simulation and experimental data indicate that the proposed framework is effective in balancing false alarms and missed alarms and detecting incipient faults, and thus would have favorable application prospects.