Maximum reweighted-kurtosis deconvolution: a fully blind and adaptive method for restoration of gear fault impulse trains

Abstract

Deconvolution based on vibration signals has been proven to be an effective tool in gear fault diagnosis. However, for many common methods, precisely restoring the fault impulse train is still a challenging task due to the great dependence on prior knowledge and the empirical determination of filter parameters. In this paper, a fully blind and adaptive method termed maximum reweighted-kurtosis deconvolution (MRKD) is proposed. A new deconvolution criterion, i.e., reweighted-kurtosis, is defined. This criterion possesses great robustness to impulse interferencesand thus has great potential to solve the problem of previous kurtosis-based methods in which a single dominant impulse is deconvolved instead of the impulse train induced by a localized fault. Furthermore, a parameter-adaptive strategy is developed to adaptively determine the appropriate filter parameters. As such, the proposed method does not require any prior knowledge of the target fault impulse train and addresses the critical issue of many common methods specifying filter parameters empirically. The proposed method is validated through simulated and real vibration signals. Comparison with the most popular deconvolution methods indicates that MRKD outperforms other methods for the restoration of a gear fault impulse train.

An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction

“…Xu et al [19] combined an optimized multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) with a novel B-spline-based energy operator to detect compound faults. Wu et al [20] designed an optimized deconvolution method named maximum reweighted-kurtosis deconvolution to recover gear fault impulses. The optimized deconvolution methods, owing to their exceptional performance, have been successfully employed in the detection of bearing fault characteristics.…”

Wavelet sparsity enhancement for extracting transient vibration signatures of bearing structural damages

“…Consequently, the overwhelming majority of studies in the field of bearing diagnostics focus on noise reduction and accurate extraction of fault signatures. A variety of effective methods have emerged, such as mode decomposition methods, 4–6 spectral kurtosis methods, 7–10 wavelet analysis methods, 11–13 blind filter methods, 14,15 and stochastic resonance methods. 16–18…”

“…Consequently, the overwhelming majority of studies in the field of bearing diagnostics focus on noise reduction and accurate extraction of fault signatures. A variety of effective methods have emerged, such as mode decomposition methods, [4][5][6] spectral kurtosis methods, [7][8][9][10] wavelet analysis methods, [11][12][13] blind filter methods, 14,15 and stochastic resonance methods. [16][17][18] Among these methods, wavelet analysis is undoubtedly one of the most popular methods, as it is a new time-frequency analysis method with multi-resolution analysis performance which is ideally suitable for characterizing the transient signatures of bearing damages.…”