Structural Diagnostics of Composite Beams Using Optimally Selected Fractional B-spline Wavelets

Katunin, Andrzej; Przystałka, P.

doi:10.1007/978-3-642-39881-0_40

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…This method, that utilizes the fast Fourier transform (FFT) algorithm, is exact despite the infinite support of the wavelets and is competitive with Mallat’s fast wavelet algorithm for orthonormal FBWs filters. 38,39 FrSWT has successful applications in many fields such as signal denoising, 32 spectral analysis, 40 pattern recognition, 41 and differential equations solving, 42 due to its fractional order of approximation and application flexibility. It should be pointed out that the FrSWT, like the most widely used classical discrete wavelet transforms, does not have the shift-invariant property due to subsampling as illustrated in Figure 5.…”

Section: Fractional Spline Wavelet Transformmentioning

confidence: 99%

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

Zhang

Wang

et al. 2022

Structural Health Monitoring

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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.

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Section: Fractional Spline Wavelet Transformmentioning

confidence: 99%

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

Zhang

Wang

et al. 2022

Structural Health Monitoring

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“…In references , Rucka and Wilde proposed to employ the continuous wavelet transform to detect damage in one‐dimensional and two‐dimensional structures via dynamic and static measurements. To solve the boundary effect in wavelet transform, Katunin used B‐spline wavelets to identify the damage on beam and plate . By aid of the compact support property and multiple knots of B‐spline wavelets, a satisfying crack identification result was presented.…”

Section: Introductionmentioning

confidence: 99%

The hybrid multivariate analysis method for damage detection

Yang

Chen

Xie

et al. 2015

Struct. Control Health Monit.

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SUMMARYAs the defects and superiorities of indices are mutualisms frequently, such as noise immunity and damage sensitivity, damage identification based on single damage index may hardly present the effective result all the time, so multiple indices fusion method is introduced in this paper to achieve some complementary improvements. In this paper, two kinds of no-baseline mode shape-based damage indices, namely, the generalized local entropy and the curvature waveform capacity fractural, are utilized to construct the basic index set for combination, and the fuzzy cluster method is introduced in order to establish fusion process. These two parts generate the hybrid multivariate analysis method finally. The multivariate analysis' superiority mainly displays on two aspects: (1) the use of no-baseline mode shape-based method ensures the damage detection efficiency with the absence of healthy mode shape serving as baseline and (2) the fusion conducted by cluster method provides the mutual support and complementation among indices, which can enhance the robustness of algorithm. The performances of the present method are verified via sufficient numerical examples, and then experiments are demonstrated on three typical engineering structures, namely, cantilever beam, blower wheel, and rotor, for further validations.

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Structural Diagnostics of Composite Beams Using Optimally Selected Fractional B-spline Wavelets

Cited by 2 publications

References 13 publications

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

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

The hybrid multivariate analysis method for damage detection

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