| Citation: | GUO Panpan, ZHANG Wenbin, CUI Ben, et al. A method for early weak fault detection and diagnosis of rolling bearing[J]. Journal of Aerospace Power, 2025, 40（3）：20230443 doi: 10.13224/j.cnki.jasp.20230443
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A method for detection and diagnosis of early weak faults in rolling bearings was proposed. It addressed the challenge of timely detection using existing methods. The method utilized the Gini index to extract feature indicators from the complete life cycle data of rolling bearings, enabling timely detection of early weak faults. Additionally, it applied enhanced singular spectral decomposition and the honey badger algorithm to optimize the maximum correlated kurtosis deconvolution method for effectively decomposing early weak fault signals in rolling bearings, maximizing noise reduction and highlighting fault impact, and enabling effective diagnosis of early weak faults in rolling bearings. Tests using the Cincinnati rolling bearing lifespan dataset demonstrated that the proposed method can detect and diagnose early weak faults in rolling bearings with a lead time of
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