scholarly journals Monitoring of Changes Signal Acoustic Emission Signals Using Waveguides

2017 ◽  
Vol 65 (4) ◽  
pp. 1317-1322 ◽  
Author(s):  
Jaroslav Začal ◽  
Petr Dostál ◽  
Michal Šustr ◽  
David Dobrocký
Keyword(s):  
Experimental Study ◽  
Stainless Steel ◽  
Acoustic Emission ◽  
Signal Detection ◽  
Piezoelectric Sensors ◽  
Material Surface ◽  
Medium Density ◽  
Medium Density Fibreboard ◽  
Ae Signal ◽  
Signal Dampening

This paper is focused on possibilities of acoustic emission (AE) signal detection from material surface through waveguide for commonly used piezoelectric sensors. It also considers the experimental study of enhanced detection of occurrence of signal guided through waveguide corpus, its changes and deformities. Aim of this work is verification of several waveguide setup possibilities for maximization of AE signal detection in practice. For this purpose, multiple waveguide setups were manufactured from stainless steel and aluminium alloy. Hsu‑Nielson pen test was utilized for signal actuation. Results demonstrate the differences between measured AE signal with and without employment of waveguide (changes in signal course through different materials and shapes), as well as magnitude of signal dampening and amplification necessary for veritable signal interpretation. Measurements were conducted on agglomerated composite of medium density fibreboard (MDF).

scholarly journals Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding

2021 ◽  
Vol 11 (15) ◽  
pp. 7045
Author(s):  
Ming-Chyuan Lu ◽  
Shean-Juinn Chiou ◽  
Bo-Si Kuo ◽  
Ming-Zong Chen
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Frequency Domain ◽  
Monitoring System ◽  
Welding Quality ◽  
Steel Sheets ◽  
Signal Features ◽  
Ae Signal ◽  
Laser Microwelding ◽  
Ae Signals

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

scholarly journals Wavelet application in acoustic emission signal detection of wire related events in pipeline

2021 ◽  
Author(s):  
Ran Wu
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Signal Detection ◽  
Time Scale ◽  
Inspection Time ◽  
Time Frequency ◽  
Pipeline Inspection ◽  
Ae Signal ◽  
Thesis Work

This thesis establishes an automatic classification program for the signal detection work in pipeline inspection. Time-scale analysis provides the basic methodology of this thesis work. The wavelet transform is implemented in the program for filtering out the majority of noise and detect needed signals. As a popular nondestructive test, acoustic emission (AE) testing has been widely used in many physical and engineering fields such as leak detection and pipeline inspection. Among those applied AE tests, a common problem is to extract the physical features of the ideal events, so as to detect similar signals. In acoustic signal processing, those features can be represented as joint time frequency distribution. However, classical signal processing methods only give global information on either time or frequency domain, while local information is lots. Although the short-time Fourier transform (STFT) is developed to analyze time and frequency details simultaneously, it can only achieve limited precision. Other time-frequency methods are also applied in AE signal processing, but they all have the problem of resolution and time consuming. Wavelet transform is a time-scale technique with adaptable precision, which makes better feature extraction and detail detection. This thesis is an application of wavelet transform in AE signal detection where various noise exists. The wavelet transform with Morelet wavelet as the mother wavelet provides the basis of the program for auto classification in this thesis work. Finally the program is tested with two industrial projects to verify the workability of wavelet transforms and the reliability of the developed auto classifiers.

Experimental Study on Acoustic Emission Signal Detection in Process of Laser-Induced Backside Wet Etching of Sapphire

2017 ◽  
Vol 44 (4) ◽  
pp. 0402003
Author(s):  
罗志良 Luo Zhiliang ◽  
谢小柱 Xie Xiaozhu ◽  
魏昕 Wei Xin ◽  
胡伟 Hu Wei ◽  
任庆磊 Ren Qinglei ◽  
...  
Keyword(s):  
Experimental Study ◽  
Acoustic Emission ◽  
Signal Detection ◽  
Acoustic Emission Signal ◽  
Wet Etching ◽  
Emission Signal

Comparative Study of Acoustic Emission Characteristics for Intergranular Corrosion of 316L Stainless Steel Parent Material and Weldment

2014 ◽  
Author(s):  
Wenjie Bai ◽  
Mengyu Chai ◽  
Lichan Li ◽  
Quan Duan
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Comparative Analysis ◽  
Intergranular Corrosion ◽  
316L Stainless Steel ◽  
Signal Amplitude ◽  
Parent Material ◽  
Ae Signal ◽  
Preliminary Corrosion ◽  
Spectrum Characteristics

The 316L stainless steel parent material and weldment specimens were made to carry out intergranular corrosion(IGC) test using the method of boiling nitric acid. During the corrosion experiment, the acoustic emission(AE) signals were collected. Through the comparative analysis of corrosion rate and metallographic structure, the results showed that the IGC of parent material and weldment can be divided into the preliminary corrosion stage and the rapid corrosion stage. The AE parameters and spectrum characteristics of the two corrosion stages of the parent material and weldment were analyzed. The results showed that: in preliminary and rapid corrosion stages, the AE signal amplitude and energy of weldment were higher than that of parent material; the spectrum characteristics of weldment was more abundant than that of parent material. Based on the results of the comparative analysis, the AE sources of parent material and weldment IGC and the possibilities of monitoring IGC using AE technique were analyzed.

scholarly journals Wavelet application in acoustic emission signal detection of wire related events in pipeline

2021 ◽  
Author(s):  
Ran Wu
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Signal Detection ◽  
Time Scale ◽  
Inspection Time ◽  
Time Frequency ◽  
Pipeline Inspection ◽  
Ae Signal ◽  
Thesis Work

This thesis establishes an automatic classification program for the signal detection work in pipeline inspection. Time-scale analysis provides the basic methodology of this thesis work. The wavelet transform is implemented in the program for filtering out the majority of noise and detect needed signals. As a popular nondestructive test, acoustic emission (AE) testing has been widely used in many physical and engineering fields such as leak detection and pipeline inspection. Among those applied AE tests, a common problem is to extract the physical features of the ideal events, so as to detect similar signals. In acoustic signal processing, those features can be represented as joint time frequency distribution. However, classical signal processing methods only give global information on either time or frequency domain, while local information is lots. Although the short-time Fourier transform (STFT) is developed to analyze time and frequency details simultaneously, it can only achieve limited precision. Other time-frequency methods are also applied in AE signal processing, but they all have the problem of resolution and time consuming. Wavelet transform is a time-scale technique with adaptable precision, which makes better feature extraction and detail detection. This thesis is an application of wavelet transform in AE signal detection where various noise exists. The wavelet transform with Morelet wavelet as the mother wavelet provides the basis of the program for auto classification in this thesis work. Finally the program is tested with two industrial projects to verify the workability of wavelet transforms and the reliability of the developed auto classifiers.

Detection of corrosion on 304 stainless steel by acoustic emission measurement

2010 ◽  
Vol 57 (3) ◽  
pp. 126-132 ◽  
Author(s):  
Gang Du ◽  
Weikui Wang ◽  
Shizhe Song ◽  
Shijiu Jin
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Constant Load ◽  
Corrosion Process ◽  
304 Stainless Steel ◽  
Emission Measurement ◽  
Nacl Solution ◽  
Content Type ◽  
Ae Signal ◽  
Ae Signals

PurposeThe purpose of this paper is to report an investigation of the acoustic emission (AE) characteristics of the corrosion process of 304 stainless steel in acidic NaCl solution.Design/methodology/approachThe corrosion behavior of a specimen with constant load in acidic NaCl solution was studied, and the AE signal characteristics of the corrosion process were analyzed. Stress corrosion cracking of the specimen was detected using the AE and electrochemical noise (EN) techniques, and the acquired data were compared.FindingsThe results indicated that AE technology is very sensitive to the AE signals generated by 304 nitrogen controlled stainless steel in acidic NaCl solution. The characteristics of AE signals at different stages of the corrosion process are significantly different. Additionally, the AE test result is confirmed by the EN test results.Originality/valueThe characteristics of AE signals at different stages of the corrosion process are gained for the first time, which is an important guide by which to distinguishing different stages of corrosion.

In-Situ Observation and Acoustic Emission Analysis for Corrosion Pitting of MgCl2 Droplet in SUS304 Stainless Steel

2012 ◽  
Vol 53 (6) ◽  
pp. 1069-1074 ◽  
Author(s):  
Mitsuharu Shiwa ◽  
Hiroyuki Masuda ◽  
Hisashi Yamawaki ◽  
Kaita Ito ◽  
Manabu Enoki
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Emission Analysis ◽  
Sus304 Stainless Steel ◽  
Corrosion Pitting ◽  
Acoustic Emission Analysis

scholarly journals The duration-energy-size enigma for acoustic emission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Blai Casals ◽  
Karin A. Dahmen ◽  
Boyuan Gou ◽  
Spencer Rooke ◽  
Ekhard K. H. Salje
Keyword(s):  
Acoustic Emission ◽  
Mean Field Theory ◽  
Mean Field ◽  
Maximum Amplitude ◽  
Time Profile ◽  
Power Law Distribution ◽  
Model Predictions ◽  
Ae Signal ◽  
Strain Signal ◽  
Better Than

AbstractAcoustic emission (AE) measurements of avalanches in different systems, such as domain movements in ferroics or the collapse of voids in porous materials, cannot be compared with model predictions without a detailed analysis of the AE process. In particular, most AE experiments scale the avalanche energy E, maximum amplitude Amax and duration D as E ~ Amaxx and Amax ~ Dχ with x = 2 and a poorly defined power law distribution for the duration. In contrast, simple mean field theory (MFT) predicts that x = 3 and χ = 2. The disagreement is due to details of the AE measurements: the initial acoustic strain signal of an avalanche is modified by the propagation of the acoustic wave, which is then measured by the detector. We demonstrate, by simple model simulations, that typical avalanches follow the observed AE results with x = 2 and ‘half-moon’ shapes for the cross-correlation. Furthermore, the size S of an avalanche does not always scale as the square of the maximum AE avalanche amplitude Amax as predicted by MFT but scales linearly S ~ Amax. We propose that the AE rise time reflects the atomistic avalanche time profile better than the duration of the AE signal.

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