Damage detection using wavelet entropy of acoustic emission waveforms in concrete under flexure

2020 ◽  
pp. 147592172095709
Author(s):  
Nitin Burud ◽  
JM Chandra Kishen
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Time Domain ◽  
Fracture Process ◽  
Damage Index ◽  
Relative Energy ◽  
Wavelet Entropy ◽  
Complex Fracture ◽  
The Time Domain ◽  
Entropy Formulation

This work dives into the spectral realm of acoustic emission waveforms. The acoustic emission waveforms carry a footprint of source, its mechanism, and the information of the medium through which it travels. The idiosyncrasies of these waveforms cannot be visualized from the time-domain parameters. The complex fracture process of the heterogeneous composite, such as concrete, reflects in the spectral disorder of acoustic emission signals. The use of wavelet entropy is proposed to estimate the spectral disorder. To evaluate wavelet entropy, the relative energy distribution in frequency sub-bands is determined using the wavelet transform. The Shannon entropy formulation as a wavelet entropy is utilized for discriminating spatiotemporally distributed acoustic emission events according to their respective level of disorder. The possible twofold application of the wavelet entropy as a signal discriminator and a damage index is qualitatively demonstrated. The increase in the statistical variance of wavelet entropy distribution with the increase in stress level reveals the presence of multi-sources as well as multi-mechanistic fracture process.

An algorithm for Morlet wavelet transform based on generalized discrete Fourier transform

2019 ◽  
Vol 17 (05) ◽  
pp. 1950030
Author(s):  
Hua Yi ◽  
Peichang Ouyang ◽  
Tao Yu ◽  
Tao Zhang
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Discrete Fourier Transform ◽  
Time Domain ◽  
Morlet Wavelet ◽  
Wavelet Function ◽  
Linear Convolution ◽  
Calculation Results ◽  
Morlet Wavelet Transform ◽  
The Time Domain

Continuous wavelet transform (CWT) is a linear convolution of signal and wavelet function for a fixed scale. This paper studies the algorithm of CWT with Morlet wavelet as mother wavelet by using nonzero-padded linear convolution. The time domain filter, which is a non-causal filter, is the sample of wavelet function. By making generalized discrete Fourier transform (GDFT) and inverse transform for this filter, we can get a geometrically weighted periodic extension of the filter when evaluated outside its original support. From this extension of the time domain filter, we can get a causal filter. In this paper, GDFT-based algorithm for CWT, which has a more concise form than that of linear convolution proposed by Jorge Martinez, is constructed by using this causal filter. The analytic expression of the GDFT of this filter, which is essential for GDFT-based algorithm for CWT, is deduced in this paper. The numerical experiments show that the calculation results of GDFT-based algorithm are stable and reliable; the running speed of GDFT-based algorithm is faster than that of the other two algorithms studied in our previous work.

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.

scholarly journals Application of the Acoustic Emission Method for Diagnosis of On-Load Tap Changer

2017 ◽  
Vol 42 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Henryk Majchrzak ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Operating Conditions ◽  
Time Frequency ◽  
Measurement Results ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Short Time

Abstract This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.

Research on Feature Extraction of Acoustic Emission Signals in Time-Domain

2013 ◽  
Vol 819 ◽  
pp. 171-175 ◽  
Author(s):  
Wei Wang ◽  
Qiang Li
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Time Domain ◽  
Industrial Environment ◽  
Bearing Fault ◽  
Partial Analysis ◽  
The Time Domain

Acoustic emission detecting has been widely used in the diagnosis of bearing fault, but nearly all of these implements require that the transducer placed close to the source of acoustic emission. However, in actual industrial environment, the transducer couldnt be mounted very close to the bearings. In this paper, the time-domain wave and time-domain features based methods were analyzed and compared among four channels at different rotating speeds. And partial analysis and some conclusions drawn from the analysis were listed below.

ESD Affected GMR Head Detection by Using Machine Model: Wavelet Transform Technique Approach

2009 ◽  
Vol 152-153 ◽  
pp. 443-446
Author(s):  
N. Suwannata ◽  
D. Sompongse ◽  
P. Rakpongsiri ◽  
Apirat Siritaratiwat
Keyword(s):  
Wavelet Transform ◽  
Time Domain ◽  
Response Signal ◽  
Machine Model ◽  
Recording Head ◽  
Test Parameter ◽  
Recording Heads ◽  
Head Detection ◽  
Magnetic Recording Head ◽  
The Time Domain

This report proposes the wavelet transform technique using the 4th Daubechies order to detect glitches on a magnetic recording head signal in the time-domain. It is found that the glitch occurs when the electrostatic discharged (ESD) level of the machine model (MM) on giant magnetoresistive (GMR) heads is in the range of 6-9 V. The electrical test parameter and scanning electron microscope (SEM) photograph of recording heads shows no change in reader sensor. However, the parameter and SEM results clearly show the visible GMR damage when the MM-ESD voltage (VESD) is 10 V. The glitch in magnetic response signal of the GMR head occurs when the VESD is increased. Therefore, the wavelet transform technique can be a novel instrument to forecast the GMR degradation due to the MM-ESD effect.

Time-Frequency Analyzing of Acoustic Emission Signals in Drilling of Glass/Epoxy Composites

2012 ◽  
Author(s):  
Hossein Heidary ◽  
Navid Zarif Karimi ◽  
Mehdi Ahmadi Najafabadi ◽  
Giangiacomo Minak ◽  
Andrea Zucchelli
Keyword(s):  
Acoustic Emission ◽  
Time Domain ◽  
Wavelet Packet ◽  
Matrix Cracking ◽  
Drilling Process ◽  
Damage Mechanisms ◽  
Time Frequency ◽  
Pull Out ◽  
The Time Domain ◽  
Stationary Behavior

Drilling is a dynamic process which causes some defects in composite materials such as delamination, fiber pull out and matrix cracking. Because of non-stationary behavior of drilling process, using online method to monitor these damage mechanisms is inevitable. In this paper, acoustic emission signals and wavelet analysis are applied to monitor drilling action from entry to exit. The results show that the selected monitoring indices from the time domain parameters and wavelet packet coefficients are capable of detecting the drilling stages and damage mechanisms during the process effectively.

Adaptive Digital Filtering and Its Algorithms for Acoustic Echo Canceling

2011 ◽  
pp. 225-285
Author(s):  
Mohammad Reza Asharif ◽  
Rui Chen
Keyword(s):  
Correlation Function ◽  
Wavelet Transform ◽  
Computational Complexity ◽  
Frequency Domain ◽  
Time Domain ◽  
Digital Filtering ◽  
Wiener Filtering ◽  
Acoustic Echo ◽  
The Time Domain ◽  
New System

In this chapter, we shall study adaptive digital filtering (ADF) and its application to acoustic echo canceling (AEC). At first, Wiener filtering and algorithms such as LMS in the time domain for ADF are explained. Then, to decrease the computational complexity, the frequency domain algorithms such as FDAF and FBAF will be studied. To challenge the double-talk problem in AEC, we will also introduce various algorithms by processing the correlation function of the signal. The proposed algorithms here are CLMS, ECLMS, and using frequency domain is FECLMS, and using wavelet transform is WECLMS. Each of these algorithms has its own merits, and they will be evaluated. At the end of this chapter a new system for room-acoustic partitioning is proposed. This new system is called smart acoustic room (SAR). The SAR will also be used in AEC with double-talk condition. The authors wish to gather all aspects in studying ADF and their use in AEC by going very deep into theoretical details as well as considering more practical and feasible applications considering real-time implementation.

Application of Wavelet Analysis in the Virtual Measurement of Roundness/Cylindricalness for Shaft-Like Components

2008 ◽  
Vol 392-394 ◽  
pp. 69-73
Author(s):  
Li Zhi Gu ◽  
Chun Jiang Xiang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Time Domain ◽  
Least Square Method ◽  
Virtual Manufacturing ◽  
Least Square ◽  
Time Saving ◽  
Individual Step ◽  
The Time Domain

Wavelet analysis was applied to detect the greatest deviation from the perfect circle for the roundness and cylindricalness of shaft-like components in the virtual manufacturing engineering. Based on the concepts of the roundness and cylindricalness, the least square method was adopted into the determination of the reference—the least square circle from which the deviation was calculated. In order to obtain the extremum of the errors, wavelet transform was carried out with binary wavelet from the time domain to frequency domain. A virtual probe was used to have the signals for the measured point by using WTnode_gettranslation() function. The accuracy of the measurement depends, to a large extent, on the length of individual step along the axis and the density of the measured points on the sections. Experiments have shown that the measuring processing was time-saving and the results from the wavelet analysis were much reliable.

Apodisation in the time domain

1992 ◽  
Vol 2 (4) ◽  
pp. 615-620
Author(s):  
G. W. Series
Keyword(s):  
Time Domain ◽  
The Time Domain
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