Gearbox Fault Diagnosis Based on Parallel RSSD and Improved MOMEDA
In order to overcome the limitations of traditional resonance sparse signal decomposition and mom,and improve its ability to extract weak fault features,a parallel dual parameter optimization RSSD and improved MOMEDA method for plan-etary gearbox fault diagnosis was proposed.Firstly,the parallel two parameter optimization RSSD constructed wavelet basis func-tions matching with different fault characteristics,and decomposed the composite fault signal into different resonance compo-nents adaptively to realize the decoupling of complex fault features.Secondly,the resonance component was deconvoluted by the improved MOMEDA,which effectively eliminated the influence of complex transmission path and strong environmental noise,and enhanced the weak fault related pulse.Finally,through the analysis of the actual fault signals of the planetary gearbox ex-perimental platform,it is proved that the proposed method not only has good decoupling performance and the ability to extract weak fault signals,but also can comprehensively and accurately extract different types of faults.
Resonance Sparse Signal DecompositionMultipoint Optimal Minimum Entropy DeconvolutionPlan-etary GearboxFault Diagnosis
万方数据
维普
