Application of Improved MCKD-MEEMD in Fault Diagnosis of Rolling Bearings
In order to solve the problem that the fault signal is covered by noise in actual working conditions and the fault charac-teristic frequency is difficult to extract,a rolling bearing fault diagnosis method combining improved maximum kurtosis deconvo-lution(MCKD)and improved lumped average empirical mode decomposition(MEEMD)is proposed.First,it is proposed to use synthetic kurtosis as an index to select the optimal parameters of MCKD:the number of displacements M and the maximum filter length L;then the optimal parameters are substituted into the MCKD algorithm to obtain the best noise reduction signal;finally,the noise reduction signal is used MEEMD decomposes to obtain a number of intrinsic modal components(IMF),selects appropri-ate components for signal reconstruction,and then performs spectrum analysis on the reconstructed signal.In the spectrum,the fault frequency and other information can be found.The advantages and disadvantages of the MEEMD method are analyzed through simulation,and the deficiencies are improved by using the improved MCKD method.The improved MCKD-MEEMD method is compared with the MEEMD method and the traditional MCKD-MEEMD method,and the improvement is proved.The fault diagnosis effect of MCKD-MEEMDmethod is better.
Maximum Correlation Kurtosis DeconvolutionSynthetic KurtosisEmpirical Mode DecompositionFa-ult Diagnosis
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