No 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.

Wavelet methods are widely used in mechanical transient vibration signature detection and fault diagnosis. Undesirable artifacts (e.g., spurious noise spikes and pseudo-Gibbs components) are serious issues for the mainstream methods, probably resulting in inaccurate analysis results. For this reason, a new wavelet sparsity enhancement methodology is proposed to achieve artifact-free extraction of bearing transient vibration signatures. The wavelet sparsity is enhanced through two aspects—that is, wavelet basis design and wavelet coefficient processing. Specifically, we use a new family of wavelets called fractional B-spline wavelets on bearing fault signal analysis and propose the adaptive undecimated fractional spline wavelet transform which addresses the shift-invariant problem and also permits to customize an optimal wavelet basis according to the signal itself adaptively. Meanwhile, we introduce a two-step wavelet processing method including a nonlinear operator and a generalized hard-thresholding (with symmetric or asymmetric thresholds determined automatically by wavelet coefficients at each level). Moreover, unlike the most existing wavelet methods, the approximation coefficients are also processed along with the detailed coefficients to remove the possible low-frequency noise. The final transient vibration signatures are reconstructed with the processed coefficients and would be sparser, more accurate, and almost free of artifacts. The validity of the methodology is verified with the analysis results of vibration signals measured from fault-injection experiment and industrial wind turbine transmission system. The comparisons highlight the advantages of the methodology over several common methods in suppressing artifacts and extracting the sparse transient vibration signatures of bearing structural damages.