Characteristic Analysis of Welding Crack Acoustic Emission Signals Using Synchrosqueezed Wavelet Transform

2018 ◽  
Vol 46 (6) ◽  
pp. 20170218
Author(s):  
Kuanfang He ◽  
Qi Li ◽  
Qing Yang
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Characteristic Analysis ◽  
Synchrosqueezed Wavelet Transform

Rail crack monitoring based on Tsallis synchrosqueezed wavelet entropy of acoustic emission signals: A field study

2017 ◽  
Vol 17 (6) ◽  
pp. 1410-1424 ◽  
Author(s):  
Dan Li ◽  
Kevin Sze Chiang Kuang ◽  
Chan Ghee Koh
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Time Window ◽  
Field Tests ◽  
Local Variation ◽  
Monitoring Strategy ◽  
Wavelet Entropy ◽  
Time Frequency ◽  
Crack Monitoring ◽  
Synchrosqueezed Wavelet Transform

This article focuses on the rail crack monitoring using acoustic emission technique in the field typically with complex cracking conditions and high operational noise. A novel crack monitoring strategy based on Tsallis synchrosqueezed wavelet entropy was developed, where synchrosqueezed wavelet transform was introduced to explore the time–frequency characteristics of acoustic emission signals and Tsallis entropy was adopted to quantify the local variation of acoustic emission wavelet coefficients more accurately. The mother wavelet of synchrosqueezed wavelet transform and three key parameters of time-Tsallis synchrosqueezed wavelet entropy, including characteristic frequency band, non-extensive parameter, and time window length, were appropriately determined. The performance of the strategy was validated through field tests with an incipient rail crack and trains running at operating speeds. Time-Tsallis synchrosqueezed wavelet entropy efficiently detected and located the crack by extracting the crack-related transients in acoustic emission signals that were easily submerged in the operational noise. Synchrosqueezed wavelet transform further helped to analyze the mechanisms of these crack-related transients, which were distinguished to be either crack propagation or impact. The experimental results demonstrated that the crack monitoring strategy proposed is able to detect both surface and internal rail cracks even in the noisy environment, highlighting its potential for field applications.

A new acoustic emission damage localization method using synchrosqueezed wavelet transforms picker and time-order method

2020 ◽  
pp. 147592172097704
Author(s):  
Jingkai Wang ◽  
Linsheng Huo ◽  
Chunguang Liu ◽  
Gangbing Song
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Damage Localization ◽  
Order Method ◽  
Time Difference Of Arrival ◽  
Localization Method ◽  
Time Frequency ◽  
Steel Bar ◽  
Time Order ◽  
Synchrosqueezed Wavelet Transform

Acoustic emission technique, as a passive structural health monitoring technique, has been widely applied to detecting and locating the structural damage. The time difference of arrival and the wave velocity are the key factors in most of the acoustic emission localization methods, and the accuracy of these two factors will affect the accuracy of damage localization. To improve the accuracy of damage localization, this article proposes a new damage localization method based on the synchrosqueezed wavelet transform picker and the time-order method. The synchrosqueezed wavelet transform picker, which picks the time–frequency similar point based on time–frequency similarity theory in the low-noise interval of time–frequency matrix, can improve the accuracy and robustness of calculating time difference of arrival. Meanwhile, the time-order method not only measures the wave velocity in real time but also reduces the computing time by appropriately arranging the distribution of acoustic emission sensors. These advantages improve the accuracy and robustness of acoustic emission localization, which was verified by experiments. Furthermore, the new localization method was employed to study the energy distribution in the embedded section of steel bar during the pull-out test of steel bar and concrete, and the results show the types of resistance between steel bar and concrete.

Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network

2020 ◽  
pp. 147592172092279 ◽  
Author(s):  
Dan Li ◽  
Yang Wang ◽  
Wang-Ji Yan ◽  
Wei-Xin Ren
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Crack Propagation ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Time Frequency ◽  
Crack Monitoring ◽  
Synchrosqueezed Wavelet Transform

This study focuses on the acoustic emission wave classification for the sake of more accurate and comprehensive rail crack monitoring in the field typically with complex cracking conditions, high-operational noise, and mass data. There are mainly three types of acoustic emission waves induced by operational noise, impact, and crack propagation, respectively. Synchrosqueezed wavelet transform was introduced to represent intrinsic characteristics of acoustic emission waves more clearly in the time–frequency domain, where acoustic emission waves induced by different mechanisms were found to show various patterns of energy distribution. Then, a multi-branch convolutional neural network model with two branches was developed to automatically classify the three types of acoustic emission waves by taking into account their synchrosqueezed wavelet transform plots in various time–frequency scales. Training, validation, and test data sets were constructed using acoustic emission waves collected through a series of field and laboratory tests with various noise levels and loading conditions. The transfer learning was used to train the model faster, and the Bayesian optimization algorithm was applied to tune the hyperparameters. Finally, the multi-branch convolutional neural network model achieved higher accuracy and robustness than the traditional convolutional neural network model of single branch in identifying different acoustic emission mechanisms. The proposed acoustic emission wave classification method based on synchrosqueezed wavelet transform and multi-branch convolutional neural network is able to detect not only surface rail cracks, where both impact-induced and crack propagation-induced acoustic emission waves would be identified, but also internal rail cracks where only crack propagation-induced acoustic emission waves would be captured.

scholarly journals Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform

2020 ◽  
Vol 28 (2) ◽  
pp. 390-398 ◽  
Author(s):  
Takafumi Kinoshita ◽  
Koichi Fujiwara ◽  
Manabu Kano ◽  
Keiko Ogawa ◽  
Yukiyoshi Sumi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Sleep Spindle ◽  
Synchrosqueezed Wavelet Transform

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

2021 ◽  
pp. 095441192110480
Author(s):  
Lakshmi M Hari ◽  
Gopinath Venugopal ◽  
Swaminathan Ramakrishnan
Keyword(s):  
Wavelet Transform ◽  
Biceps Brachii ◽  
Singular Value ◽  
Time Frequency ◽  
Singular Value Decomposition Method ◽  
Dynamic Contractions ◽  
Synchrosqueezed Wavelet Transform ◽  
Value Decomposition ◽  
Neuromuscular Conditions ◽  
Semg Signals

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

scholarly journals Acoustic Emission Wavelet Analysis of Filament Wound Composite Tube Fatigue Damage Process

2015 ◽  
Vol 9 (1) ◽  
pp. 214-219 ◽  
Author(s):  
Su Hua ◽  
Chang Cheng
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Fatigue Damage ◽  
Frequency Band ◽  
Low Frequency ◽  
High Signal ◽  
Wave Form ◽  
Radial Compression ◽  
Time Frequency ◽  
Emission Signal

This paper performed a radial compression fatigue test on glass fiber winding composite tubes, collected acoustic emission signals at different fatigue damages stages, used time frequency analysis techniques for modern wavelet transform, and analyzed the wave form and frequency characteristics of fatigue damaged acoustic emission signals. Three main frequency bands of acoustic emission signal had been identified: 80-160 kHz (low frequency band), 160-300 kHz (middle frequency band), and over 300kHz (high frequency band), corresponding to the three basic damage modes: the fragmentation of matrix resin, the layered damage of fiber and matrix, and the fracture of cellosilk respectively. The usage of wavelet transform enabled the separation of fatigue damaged acoustic emission signals from interference wave, and the access to characteristics of high signal-noise-ratio fatigue damage.

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