Performance of wavelet denoising in vibration analysis: highlighting

Chiementin, Xavier; Kilundu, Bovic; Rasolofondraibe, Lanto; Crequy, Samuel; Pottier, Bernard

doi:10.1177/1077546311412992

Cited by 31 publications

(25 citation statements)

References 21 publications

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“…e hard threshold method is simple to use, but the overall function is discontinuous, which will lead to additional vibration phenomenon of the reconstructed signal. Although the soft threshold method is continuous as a whole, the wavelet with a large amplitude will generate the attenuation phenomenon, which will cause a constant deviation of the processed signal [5,25]. Given the deficiencies of hard and soft threshold methods, this paper constructs a new threshold function based on the hard and soft threshold functions.…”

Section: Wavelet Reshold Denoising Methodmentioning

confidence: 99%

An EEMD‐Based Denoising Method for Seismic Signal of High Arch Dam Combining Wavelet with Singular Spectrum Analysis

Zhang

et al. 2019

Shock and Vibration

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Due to complicated noise interference, seismic signals of high arch dam are of nonstationarity and a low signal-to-noise ratio (SNR) during acquisition process. The traditional denoising method may have filtered effective seismic signals of high arch dams. A self-adaptive denoising method based on ensemble empirical mode decomposition (EEMD) combining wavelet threshold with singular spectrum analysis (SSA) is proposed in this paper. Based on the EEMD result for seismic signals of high arch dams, a continuous mean square error criterion is used to distinguish high-frequency and low-frequency components of the intrinsic mode functions (IMFs). Denoised high-frequency IMF using wavelet threshold is reconstructed with low-frequency components, and SSA is implemented for the reconstructed signal. Simulation signal denoising analysis indicates that the proposed method can significantly reduce mean square error under low SNR condition, and the overall denoising effect is superior to EEMD and EEMD-Wavelet threshold denoising algorithms. Denoising analysis of measured seismic signals of high arch dams shows that the performance of denoised seismic signals using EEMD-Wavelet-SSA is obviously improved, and natural frequencies of the high arch dams can be effectively identified.

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Section: Wavelet Reshold Denoising Methodmentioning

confidence: 99%

An EEMD‐Based Denoising Method for Seismic Signal of High Arch Dam Combining Wavelet with Singular Spectrum Analysis

Zhang

et al. 2019

Shock and Vibration

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“…small echoes that come from defects. The threshold for the de-noising is obtained by a wavelet coefficients selection rule using a penalization method provided by Birgé-Massart, which produces good results [48]. In contrast to other digital filters, the Wavelet de-noising filter does not produce an unwanted distortion of the signal characteristic parameters, e.g.…”

Section: Novel Approachmentioning

confidence: 99%

Maintenance management based on Machine Learning and nonlinear features in wind turbines

et al. 2020

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Delamination is a common problem in wind turbine blades, creating stress concentration areas that can lead to the partial or complete rupture of the blade. This paper presents a novel delamination classification approach for reliability monitoring systems in wind turbine blades. It is based on the feature extraction of a nonlinear autoregressive with exogenous input system (NARX) and linear auto-regressive model (AR). A novelty in this paper is NARX as a Feature Extraction method for wind turbine blade delamination classification. Further, the NARX feature is demonstrated to be significantly better than linear AR feature for blade damage detection, and NARX can describe the inherent nonlinearity of blade delamination correctly. A real case study considers different levels of delamination employing ultrasonic guided waves that are sensitive to delamination. Firstly, the signals obtained are filtered and de-noised by wavelet transforms. Then, the features of the signal are extracted by NARX, and the number of features is selected considering the Neighbourhood Component Analysis as main novelties. Finally, six scenarios with different delamination sizes have been performed by supervised Machine Learning

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“…Therefore, a new combined denoising model is used here. This model uses a Wavelet-SG algorithm which was developed based on the wavelet transform [30] and Savitzky-Golay (SG) theory [32] as pre-filter, and used Ensemble Empirical Mode Decomposition(EEMD) [31] The signal processing flow chart of using this Wavelet-SG-EEMD denoising model is shown in Fig. 15, and the detailed procedures are as follows.…”

Section: Wavelet-sg-eemd Denoising Modelmentioning

confidence: 99%

“…The dynamic characteristics of the leaf spring were investigated experimentally on a test rig which was designed based on the double potential well theory [27]. A newly developed signal processing method, Wavelet-SG-EEMD )Wavelet, Savitzky-Golay (SG) and Ensemble Empirical Mode Decomposition(EEMD)) [29][30][31][32], were used to reduce noise and help to identify chaos features of the vibration signal generated by the system. A phase space reconstruction method, based on the first minimum mutual information (FMMI) theory, was used to obtain the chaotic attractors.…”

Section: Introductionmentioning

confidence: 99%

Study of double-potential-well leaf spring system’s chaotic vibration

Liu¹,

Jian²,

Su³

et al. 2017

J. vibroeng.

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Chaotic vibration has become increasingly popular in the study of acoustic and vibration engineering. Many engineering designs have taken advantage of the special characteristics of chaos, and deliberately introduced it into the system to improve efficiency. As an important component, leaf springs have long been used in the suspension system of wheeled vehicles. Recent development is considering chaotic vibration in the design of leaf springs to improve the system's reliability. However, little experimental research has been carried out to investigate the chaos characteristics of leaf springs. Meanwhile, a preliminary study showed that some of the conventional signal processing methods may not be able to successfully identify the chaos features from a leaf spring test rig due to the complexity of the practical signal. Therefore, in this paper, a leaf spring system's chaotic vibration and relevant signal processing strategy were investigated in theory and experiment. Firstly, the relationship between the amplitude and frequency of the double potential well system is derived with averaging method. The stability is analyzed on the Vander pol plane and the global bifurcation diagram and Lyapunov exponent spectrum are applied to determine the chaotic regime accurately. Numerical simulation was conducted using a finite element method to give an idea of the leaf spring's natural frequencies where chaotic vibration can be potentially generated. The experimental rig was then designed based on double potential well theory to generate stable and repeatable chaotic vibration, and an experimental study was carried out to investigate the system's response characteristics under different excitation strengths and frequencies. An improved signal processing method, Wavelet-SG-EEMD (Wavelet, Savitzky-Golay (SG) and Ensemble Empirical Mode Decomposition (EEMD)), was used to reduce noise and beneficial to identify chaotic features of the vibration signal generated by the system. The nonlinear vibration response features of the system were carefully analyzed. Sub-harmonic phenomena, periodic modes and chaotic behavior were discovered during the experiment.

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Performance of wavelet denoising in vibration analysis: highlighting

Cited by 31 publications

References 21 publications

An EEMD‐Based Denoising Method for Seismic Signal of High Arch Dam Combining Wavelet with Singular Spectrum Analysis

An EEMD‐Based Denoising Method for Seismic Signal of High Arch Dam Combining Wavelet with Singular Spectrum Analysis

Maintenance management based on Machine Learning and nonlinear features in wind turbines

Study of double-potential-well leaf spring system’s chaotic vibration

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