| Citation: | LYU Zhongliang, LI Lingfeng, JIA Xiangyu, et al. Fault feature extraction method of rolling bearing based on adaptive MOMEDA[J]. Journal of Aerospace Power, 2026, 41（6）：20240646 doi: 10.13224/j.cnki.jasp.20240646
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Because the environmental noise will mask the fault signal of the rolling bearing, it is arduous to extract the fault feature. To address this issue, a fireworks optimization algorithm (FWA) based on multi-point optimal minimum entropy deconvolution algorithm (MOMEDA) was presented to optimize the early fault feature extraction method of the rolling bearing under intense noise interference. In this approach, the peak factor of the envelope spectrum was regarded as the fitness value, and the global search capacity of FWA was utilized to adaptively select the optimal parameter combination of the MOMEDA method. Subsequently, the MOMEDA algorithm was employed to enhance the early fault signal. The enhanced signal was decomposed by ensemble empirical mode decomposition (EEMD), and the multi-scale fuzzy entropy feature set was constructed. Finally, the classification was identified by support vector machine (SVM). The experimental results indicated that, compared with minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD), the classification accuracy of this method increased by 12.5% and 21.7% respectively.
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