首页|Iterative Morlet wavelet with SOSO boosting strategy for impulsive feature extraction
Iterative Morlet wavelet with SOSO boosting strategy for impulsive feature extraction
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NSTL
Elsevier
? 2022 Elsevier LtdBlind deconvolution (BD) methods are very popular in noise reduction. Generally, the setting of parameters directly affects the filtering effect of BD methods. To avoid this flaw, an adaptive method called iterative Morlet wavelet filter (IMWF) is proposed in this paper. In IMWF, the traditional Morlet wavelet filter is optimized to process the signal matrix which is adaptively constructed by the power spectral density. The lifted correlation kurtosis with autocorrelation-closing analysis is adopted as the objective function to search the optimal parameter in IMWF. After that, the SOSO (i.e. strengthen, operate, subtract, operate) boosting strategy is supplemented to enhance the IMWF through an iterative algorithm. Finally, simulation and experimental cases are analyzed to demonstrate the feasibility and superiority of the IMWF for fault identification. Some popular typical methods are utilized for comparisons. The final analysis results show that IMWF is an efficient method.
NSTL
Elsevier
