Blind deconvolution criterion based on Fourier–Bessel series expansion for rolling element bearing diagnostics

Soave, Elia; D’Elia, Gianluca; Cocconcelli, Marco; Battarra, Mattia

doi:10.1016/j.ymssp.2021.108588

Cited by 19 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this scenario, several studies have proposed effective methods to enhance and extract bearing fault features, such as the Kurtogra method [4,5], empirical modal decomposition [6,7], variational modal decomposition [8], wavelet transform [9,10], sparse representation [11][12][13], and blind deconvolution, etc. Blind deconvolution is frequently applied to the fault detection of bearings because of its ability to recover weak periodic pulses from strong background noise signals [14,15].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective adaptive guided differential evaluation blind deconvolution and its application in bearing fault detection

Qin

Yang

Sun

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Blind deconvolution (BD) methods applied to bearing fault detection often cause inferior performance due to inaccurate input parameters. Moreover, the optimal parameters of BD vary for different speeds and fault types of bearings, which seriously undermines the applicability of BD in practical industries. In this scenario, this paper proposes a parameter-adaptive BD method (MOBD) based on the multi-objective adaptive guided differential evaluation algorithm (MOAGDE). Firstly, based on the linear discriminant analysis, the quotient of inter-class distance and intra-class distance is used to determine the superiority of common bearing fault characteristic indicators to establish the multi-objective function of MOAGDE. Then, the optimal parameters of BD are searched by MOAGDE improved by dynamic switched crowding method (DSC-MOAGDE). Finally, the bearing is judged whether or what kind of fault has occurred according to the fault type locating index proposed in this paper. The main advantage of MOBD is that only bearing speed and type priories are required to achieve online detection of bearing faults. The results of simulation and experimental signals demonstrate that MOBD significantly outperforms the traditional BD method.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-objective adaptive guided differential evaluation blind deconvolution and its application in bearing fault detection

Qin

Yang

Sun

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The wavelet transform (WT) method uses the wavelet base to decompose time signals. However, with a wavelet base, WT has no self-adaptability at different scales, and empirical mode decomposition (EMD) can separate mixed signals into several intrinsic mode function (IMF) components and identify feature signals [ 13 , 14 ]. However, there are still problems with this method [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, there are still problems with this method [ 15 , 16 ]. Though the local mean decomposition (LMD) method can apply the local average and envelope estimation functions to signals, it is limited by the endpoint effect [ 13 , 17 ]. A filtering method can sometimes eliminate noises that are not correlated with the feature leak signals and improve localization accuracy; however, these methods still have intrinsic drawbacks and external factors that limit their use in pipelines.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Leak Localization Approach Using Acoustic Emission for Industrial Pipelines

Gao

Piltan

Kim

2022

Sensors

View full text Add to dashboard Cite

Acoustic emission techniques are widely used to monitor industrial pipelines. Intelligent methods using acoustic emission signals can analyze acoustic waves and provide important information for leak detection and localization. To address safety and protect the operation of industrial pipelines, a novel hybrid approach based on acoustic emission signals is proposed to achieve reliable leak localization. The proposed method employs minimum entropy deconvolution using the maximization kurtosis norm of acoustic emission signals to remove noise and identify important feature signals. In addition, the damping frequency energy based on the dynamic differential equation with damping term is designed to extract important energy information, and a smooth envelope for the feature signals over time is generated. The zero crossing tracks the arrival time via the envelope changes and identifies the time difference of the acoustic waves from the two channels, each of which is installed at the end of a pipeline. Finally, the time data are combined with the velocity data to localize the leak. The proposed approach has better performance than the existing generalized cross-correlation and empirical mode decomposition combined with the generalized cross-correlation methods, providing proper leak localization in the industrial pipeline.

show abstract

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

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.

show abstract

Blind deconvolution criterion based on Fourier–Bessel series expansion for rolling element bearing diagnostics

Cited by 19 publications

References 25 publications

Multi-objective adaptive guided differential evaluation blind deconvolution and its application in bearing fault detection

Multi-objective adaptive guided differential evaluation blind deconvolution and its application in bearing fault detection

A Hybrid Leak Localization Approach Using Acoustic Emission for Industrial Pipelines

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

Contact Info

Product

Resources

About