To address the challenges associated with the harsh operating conditions of train axle box bearings,where fault signals are often obscured by noise and extracting fault features remains difficult,this study proposes a Variational Mode Decomposition(VMD)parameter optimization method.The method integrates envelope entropy and kurtosis into a Harmonic Mean Index(HMI)fitness function.The algorithm is validated using fault data from a proportional test bench.First,to ensure that the syn-thetic function effectively captures both the periodicity and impulsiveness of signals at comparable mag-nitudes,the harmonic mean index is introduced.This index,combining kurtosis and envelope en-tropy,serves as the fitness function,and the Pelican Optimization Algorithm(POA)is employed to perform a global search for optimal values.Second,the crucial parameters of VMD are optimized and determined using the HMI-POA algorithm,including the optimal decomposition layer number K and punishment factor α.These crucial parameters are then applied to decompose fault signals into K Intrin-sic Mode Function(IMF),with the optimal component identified based on the Weighted Kurtosis(WK)index.Finally,the envelope demodulation of the optimal component signal is performed to ex-tract the fault characteristic features of the rolling bearings.The proposed HMI-POA-VMD algorithm is validated using fault data from a proportional test bench.Its superiority is further demonstrated through comparison with traditional methods,using the Fault Feature Coefficient(FFC)as the evalua-tion criterion.Experimental results show that the proposed method significantly enhances the extrac-tion of fault frequencies.Compared to single fitness function optimization and traditional VMD,the FFC improves by 49.1%and 62.5%respetively.This highlights the method's capability to extract richer fault frequency information and accurately identify features in noisy environments.
rolling bearingvariational mode decompositionPOAharmonic mean indexfault fea-ture extraction
维普
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