To address the issue of weak fault detection in rolling bearings within aircraft engines,which is often hampered by environmental noise interference and the potential oversight of critical information,a novel weighted feature parameter information reconstruction method is proposed.This method is then applied to the fault detection of main bearings in aircraft engines.Firstly,the particle swarm optimization(PSO)algorithm is employed to optimize the parameters in the variational mode decomposition(VMD),obtaining K0 modal compo-nents.Then,a novel weighted feature parameter information reconstruction formula is proposed to reconstruct all modal components by filtering out noise components and retaining effective information to the greatest extent.Fi-nally,envelope analysis is performed on the reconstructed signals to extract bearing fault characteristic informa-tion.Through analysis of experimental fault data from intermediate bearings in aircraft engines,it is concluded that this method can reduce the interference of noise components on the overall signal,effectively highlight fault characteristic information,and diagnose weak faults in bearings under conditions of strong background noise in-terference.Therefore,it is considered an effective method for extracting and diagnosing fault characteristics in main shaft bearings of aircraft engines.Through simulation signal calculation,the signal peak factor after noise re-duction increases by 1.62 dB,which effectively enhances the impact component.
AeroengineRolling bearingReconstruction of weighted feature parameter informationFault diagnosisVariational mode decomposition
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