scholarly journals Lamb Wave-Based Damage Localization Feature Enhancement and Extraction Method for Stator Insulation of Large Generators Using VMD and Wavelet Transform

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4205
Author(s):  
Ruihua Li ◽  
Jing Luo ◽  
Bo Hu
Keyword(s):  
Wavelet Transform ◽  
Lamb Wave ◽  
Background Noise ◽  
Lamb Waves ◽  
Signal To Noise Ratio ◽  
Scattered Wave ◽  
Damage Location ◽  
Wave Detection ◽  
Mode Decomposition ◽  
Reconstructed Signal

Lamb waves are used to locate any damage in the stator insulation structure of large generators. However, it is difficult to extract the features of Lamb wave signals in a strong background noise environment, thus significantly reducing the accuracy with which the damage is located. This paper proposes a method based on variational mode decomposition (VMD) and wavelet transform to enhance and extract the location features of stator insulation damage signals of large motors. First, considering that the characteristics of VMD are sensitive to noise, the Lamb wave detection signal is decomposed, denoised, and reconstructed; the reconstructed signal is then wavelet-transformed to extract the time of flight (TOF) of the damage-scattered wave as the damage location feature; finally, the damage location is determined using the TOF features. The proposed method is experimentally tested and verified under various noise environments. The results show that the VMD and wavelet transform methods can significantly improve the signal-to-noise ratio of Lamb wave detection signals and the accuracy with which the damage is located under strong background noise. This study extends the applicability of Lamb wave-based non-destructive detection of stator insulation damage in complex environments.

scholarly journals Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3510 ◽  
Author(s):  
Zhijian Wang ◽  
Junyuan Wang ◽  
Wenhua Du
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Optimal Number ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Multiple Fault ◽  
Mode Decomposition ◽  
Fault Features ◽  
Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

WEAK LFM SIGNAL DECTECTION BASED ON WAVELET TRANSFORM MODULUS MAXIMA DENOISING AND OTHER TECHNIQUES

2010 ◽  
Vol 08 (02) ◽  
pp. 313-326 ◽  
Author(s):  
BO LE ◽  
ZHONG LIU ◽  
TIANXIANG GU
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Correlation Coefficients ◽  
Time Frequency ◽  
Auto Correlation ◽  
Wavelet Transform Modulus Maxima ◽  
Reconstructed Signal ◽  
Modulus Maxima ◽  
Domain Information

A new method for detecting weak linear frequency modulated (LFM) pulse signals buried in additive white Gaussian noise (AWGN) is presented in this paper. The method is based on the features of wavelet transform modulus maxima (WTMM) denoising and auto-correlation filtering theory. Firstly, the frequency-domain information is extracted by auto-correlation matched filtering, and is used to deduce the optimal wavelet decomposition scales. Secondly, let the signal modulus dominate on the biggest scale after the optimal scales decomposition, then keeping the signal modulus and removing the noise modulus at each scale are performed by utilizing the different propagation properties of signal and noise wavelet modulus maxima across the scales. Finally, a reconstructed signal is obtained from the reserved signal modulus with an improved signal-to-noise ratio (SNR), and is used for time-domain information extraction. At the same time, wavelet denoising depends on selecting an optimum wavelet that matches well the shape of the signal. The cross correlation coefficients between signal and db wavelets are calculated and the optimal wavelet to analysis the LFM signal is selected. Simulations show that the method can extract time-frequency information of LFM signal when SNR ≤ -6 dB .

scholarly journals Design of sEMG Acquisition Circuit and Its Adaptive EEMD Denosing Research

2022 ◽  
Vol 16 ◽  
pp. 509-516
Author(s):  
Wei Li ◽  
Wei Hu ◽  
Kun Hu ◽  
Qiang Qin
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Electrical Signal ◽  
Signal Acquisition ◽  
Semg Signal ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Human Muscles ◽  
Eemd Method

The Surface electromyography (sEMG) signal is a kind of electrical signal which generated by human muscles during contraction. It is prone to being affected by noise because of its small amplitude, so it is necessary to remove the noise in its original signal with an appropriate algorithm. Based on the traditional signal denoising indicators, a new complex indicator r has been proposed in this paper which combines three different indicator parameters, that is, Signal to Noise Ratio (SNR), correlation coefficient (R), and standard error (SE). At the same time, an adaptive ensemble empirical mode decomposition (EEMD) method named AIO-EEMD which based on the proposed indicator is represented later. To verify the effective of the proposed algorithm, an electromyography signal acquisition circuit is designed firstly for collecting the original sEMG signal. Then, the denosing performance from the designed method is been compared with empirical mode decomposition (EMD) method and wavelet transform noise reduction method, respectively. The experiment results shown that the designed algorithm can not only automatically get the numbers of the reconstructed signal numbers, but also obtain the best reduction performance.

Lamb Wave-Based Damage Location Detection Method of Composite Plate

2011 ◽  
Vol 301-303 ◽  
pp. 1260-1266
Author(s):  
Li Shao Zhang ◽  
Huan Guo Chen ◽  
Jian Min Li ◽  
Li Tian
Keyword(s):  
Group Velocity ◽  
Lamb Wave ◽  
Composite Laminates ◽  
Composite Plate ◽  
Velocity Dispersion ◽  
Lamb Waves ◽  
Group Velocity Dispersion ◽  
Dispersion Curves ◽  
Damage Location ◽  
The Difference

To understand more about Lamb waves on composite laminates damage detection features, the Lamb wave group velocity dispersion curves are calculated and plotted by using dichotomy method in MATLAB. The signal parameters are chosen according to Group velocity dispersion curves. The dynamic response signals of the composite plate are obtained by finite element method. Damage location is calculated by the actual group velocity of Lamb wave and time of flight of the difference signal before and after damage.

Time reversibility of Lamb waves in thin plates with surface-bonded piezoelectric transducers is temperature invariant at the best reconstruction frequency

2020 ◽  
pp. 147592172096512
Author(s):  
Bhabagrahi Natha Sharma ◽  
Santosh Kapuria ◽  
A Arockiarajan
Keyword(s):  
Temperature Dependence ◽  
Lamb Wave ◽  
Lamb Waves ◽  
Dominant Role ◽  
Excitation Frequency ◽  
Piezoelectric Transducers ◽  
Time Reversibility ◽  
Adhesive Layers ◽  
Wide Range ◽  
Reconstructed Signal

The Lamb wave time-reversal method has been widely proposed as a baseline-free method for damage detection in thin-walled structures. Under varying thermal environments, it would require that the time reversibility of Lamb waves is temperature invariant. In this study, we examine the temperature dependence of Lamb waves and its time reversibility using experiments and finite element simulations on isotropic plates with surface-bonded piezoelectric wafer transducers for actuation and sensing. The study is conducted at three different temperatures of the system from 25°C to 75°C for a wide range of excitation frequency. The results indicate that the time reversibility can undergo significant changes due to temperature variations depending on the excitation frequency. However, at the best reconstruction frequency corresponding to the maximum similarity of the reconstructed signal with the original input signal (proposed recently as the probing frequency), the change in the percent similarity with temperature is insignificant. The results also demonstrate that changes in the physical properties of both adhesive layers and piezoelectric transducers with temperature play a dominant role in influencing Lamb wave amplitudes. However, only the change in the characteristics of the adhesive layers is responsible for the temperature dependence of the time reversibility of Lamb waves.

Lamb-Wave-Based Damage Identification in Laminated Composite Plates

2014 ◽  
Vol 1014 ◽  
pp. 3-8
Author(s):  
Zai Lin Yang ◽  
Hamada M. Elgamal ◽  
Jian Wei Zhang
Keyword(s):  
Lamb Wave ◽  
Structural Damage ◽  
Damage Identification ◽  
Lamb Waves ◽  
Laminated Composite ◽  
Composite Plates ◽  
High Sensitivity ◽  
Scattered Wave ◽  
Propagation Path ◽  
Laminated Composite Plates

With advantages including capability of propagation over a significant distance and high sensitivity to abnormalities and inhomogeneity near the wave propagation path, Lamb waves can be energised to disseminate in a structure and any changes in material properties or structural geometry created by a discontinuity, boundary or structural damage can be identified by examining the scattered wave signals. This paper presents an overview of the Lamb-wave-based damage identification in laminated composite plates including the formulation of lamb waves in an isotropic plate.

scholarly journals Extraction method of weak underwater acoustic signal based on the combination of wavelet transform and empirical mode decomposition

2021 ◽  
Vol 12 ◽  
pp. 7
Author(s):  
Junbing Shi ◽  
Yingmin Wang ◽  
Xiaoyong Zhang ◽  
Libo Yang
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Intrinsic Mode Functions ◽  
Threshold Method ◽  
Underwater Acoustic ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Combined Algorithm

When studying underwater acoustic exploration, tracking and positioning, the target signals collected by hydrophones are often submerged in strong intermittent noise and environmental noise. In this paper, an algorithm that combines empirical mode decomposition and wavelet transform is proposed to achieve the efficient extraction of target signals in the environment with strong noise. First the calibration of baseline drift is performed on the algorithm, and then it is decomposed into different intrinsic mode functions via empirical mode. The wavelet threshold processing is conducted according to the correlation coefficient of each mode component and the original signal, and finally the signals are reconstructed. The simulation and experiment results show that compared with the conventional empirical mode decomposition method and wavelet threshold method, when the signal-to-noise ratio is low and there exist high-frequency intermittent jamming and baseline drift, the combined algorithm can better extract the target signal, laying the foundation for direction-of-arrival estimation and target positioning in the next step.

Marine seismic signal denoising using VMD with Hausdorff distance and wavelet transform

2021 ◽  
pp. 154851292110360
Author(s):  
Abhishek Kesharwani ◽  
Vaibhav Aggarwal ◽  
Shubham Singh ◽  
Rahul B R ◽  
Arvind Kumar
Keyword(s):  
Wavelet Transform ◽  
Hausdorff Distance ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Signal Interference ◽  
Signal Denoising ◽  
Low Snr ◽  
Synthetic Signal ◽  
Mode Decomposition ◽  
Marine Seismic

In marine seismic acquisitions, signal interference remains a major menace. In this paper, a denoising approach based on the Variational Mode Decomposition (VMD) combined with the Hausdorff distance (HD) and Wavelet transform (WT) is proposed. There has been substantial research in this field over the years. However, traditional denoising methods fall short of achieving satisfactory results in an extremely low signal to noise ratio (SNR) environment. The feasibility, and stability of the proposed method was validated by performing simulations in MATLAB on both a synthetic signal and a seismic signal generated using real dataset. It was found that the proposed method does well in preserving marine signals in low SNR environments, and has a superior output SNR.

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