scholarly journals STUDY ON PROPAGATION LAW OF ACOUSTIC EMISSION SIGNALS ON ANISOTROPIC WOOD SURFACE

Wood Research ◽  
2021 ◽  
Vol 66 (4) ◽  
pp. 517-527
Author(s):  
TINGTING DENG ◽  
SHUANG JU ◽  
MINGHUA WANG ◽  
MING LI
Keyword(s):  
Acoustic Emission ◽  
Wavelet Analysis ◽  
Wood Surface ◽  
Propagation Velocity ◽  
Quadratic Function ◽  
Propagation Direction ◽  
Mode Decomposition ◽  
Propagation Law ◽  
Ae Signals ◽  
Obvious Feature

In order to explore the influence of wood’s anisotropic characteristics on Acoustic Emission (AE) signals’ propagation, the law of AE signals’ propagation velocity along different directions was studied. First, The center of the specimen’s surface was took as the AE source,then 24 directions were chose one by one every 15º around the center,and 2 AE sensors were arranged in each direction to collect the original AE signals. Second, the wavelet analysis was used to denoise the original AE signals, then the AE signals were reconstructedby Empirical Mode Decomposition (EMD). Finally, time difference location method was utilized to calculate AE signals’ propagation velocity. The results demonstrate that AE signals’ propagation velocity has obvious feature of quadratic function. In the range of 90º, as the angle of propagation direction increases, the propagation velocity of the AE signals presents a downward trend.

scholarly journals Potential of Empirical Mode Decomposition for Hilbert Demodulation of Acoustic Emission Signals in Gearbox Diagnostics

2021 ◽  
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Elisabeth Clausen
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bandpass Filter ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Spectrum ◽  
Ae Signals

Abstract Background The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One signal processing technique that has been used to enhance the demodulation process for vibration signals is the empirical mode decomposition (EMD). Until now, the combination of both techniques has not yet been used to improve the fault diagnostics in gearboxes using AE signals. Purpose In this research we explore the use of the EMD to improve the demodulation process of AE signals using the Hilbert transform and enhance the representation of a gear fault in the envelope spectrum. Methods AE signals were measured on a planetary gearbox (PG) with a ring gear fault. A comparative signal analysis was conducted for the envelope spectra of the original AE signals and the obtained intrinsic mode functions (IMFs) considering three types of filters: highpass filter in the whole AE range, bandpass filter based on IMF spectra analysis and bandpass filter based on the fast kurtogram. Results It is demonstrated how the results of the envelope spectrum analysis can be improved by the selection of the relevant frequency band of the IMF most affected by the fault. Moreover, not considering a complementary signal processing technique such as the EMD prior the calculation of the envelope of AE signals can lead to a wrong fault diagnosis in gearboxes. Conclusion The EMD has the potential to reveal frequency bands in AE signals that are most affected by a fault and improve the demodulation process of these signals. Further research shall focus on overcome issues of the EMD technique to enhance its application to AE signals.

Identification of Damage Modes in Twill-Weave Composite Based on EMD of AE Event

2012 ◽  
Vol 198-199 ◽  
pp. 60-63
Author(s):  
Wen Qin Han ◽  
Jin Yu Zhou
Keyword(s):  
Acoustic Emission ◽  
Fourier Transform ◽  
Composite Materials ◽  
Acoustic Emissions ◽  
Peak Frequency ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Different Types ◽  
Twill Weave ◽  
Ae Signals

Acoustic emission (AE) monitoring is the primary technology used for the identification of different types of failure in composite materials. Tensile test were carried out on twill-weave composite specimens, and acoustic emissions were recorded from these tests. AE signals were decomposed into a set of Intrinsic Mode Functions(IMF) components by means of Empirical Mode Decomposition(EMD) , the Fast Fourier Transform (FFT) of each IMF component was performed, it was shown that the event peak frequency of each IMF component could be directly related to the materials damage modes.

scholarly journals Identification of Deformation Stage and Crack Initiation in TC11 Alloys Using Acoustic Emission

2020 ◽  
Vol 10 (11) ◽  
pp. 3674
Author(s):  
Jiaoyan Huang ◽  
Zhiheng Zhang ◽  
Cong Han ◽  
Guoan Yang
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Crack Initiation ◽  
Peak Frequency ◽  
Identification Accuracy ◽  
Energy Ratio ◽  
Frequency Bands ◽  
Mode Decomposition ◽  
Tc11 Titanium Alloy ◽  
Ae Signals

The Acoustic Emission (AE) is a widely used real-time monitoring technique for the deformation damage and crack initiation of areo-engine blades. In this work, a tensile test for TC11 titanium alloy, one of the main materials of aero-engine, was performed. The AE signals from different stages of this test were collected. Then, the AE signals were decomposed by the Variational Mode Decomposition (VMD) method, in which the signals were divided into two different frequency bands. We calculated the engery ratio by dividing the two different frequency bands to characterize TC11′s degree of deformation. The results showed that when the energy ratio was −0.5 dB, four stages of deformation damage of the TC11 titanium alloy could be clearly identified. We further combined the calculated Partial Energy Ratio (PER) and Weighted Peak Frequency (WPF) to identify the crack initiation of the TC11 titanium alloy. The results showed that the identification accuracy was 96.33%.

Wavelet analysis of acoustic emission signals in boring

2000 ◽  
Vol 214 (5) ◽  
pp. 421-424 ◽  
Author(s):  
X Li ◽  
J Wu
Keyword(s):  
Acoustic Emission ◽  
Wavelet Analysis ◽  
Tool Wear ◽  
Metal Cutting ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Experimental Results ◽  
Cutting Processes ◽  
Ae Signals

Using acoustic emission (AE) signals to monitor tool wear states is one of the most effective methods used in metal cutting processes. As AE signals contain information on cutting processes, the problem of how to extract the features related to tool wear states from these signals needs to be solved. In this paper, a wavelet packet transform (WPT) method is used to decompose continuous AE signals during cutting; then the features related to tool wear states are extracted from decomposed AE signals. Experimental results verified the feasibility of using the WPT method to extract features related to tool wear states in boring.

A Method for Extracting Acoustic Emission Signal Frequency Characteristics of Polluted-Insulator Discharge

2014 ◽  
Vol 494-495 ◽  
pp. 1513-1516
Author(s):  
Man Cheng Yi ◽  
Jian Fang ◽  
Yong Wang ◽  
Zhi Ning Li ◽  
Chun Hui Gu ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Arc Discharge ◽  
Experimental Studies ◽  
Frequency Characteristics ◽  
Signal Frequency ◽  
Emission Signal ◽  
External Insulation ◽  
Mode Decomposition ◽  
Insulation Status ◽  
Ae Signals

Experimental studies have shown that Acoustic Emission (AE) signals generated by the polluted-insulator discharge contain information of discharge energy. To extract the frequency characteristics of AE signals in polluted-insulator discharge, algorithm combining the empirical mode decomposition (EMD) and fast fourier transform (FFT) is used. Through a great many of artificial contamination experiments, the AE signals in different contamination discharge stages are collected, and the frequency characteristics can be extracted by the method presented in this article. The results show that the frequency characteristics of AE signals can be effectively extracted by the proposed method, which gives the right corresponding relationship between frequency characteristics and the polluted-insulator corona, partial and arc discharge. It also provides technical support for monitoring the intensity of polluted-insulator discharge and the change of external insulation status. The method for extracting AE signal frequency characteristics proposed in this paper has been applied to on-line monitoring of polluted-insulator external insulation status and good results have been achieved.

scholarly journals Classification of acoustic emission signals in wood damage and fracture process based on empirical mode decomposition, discrete wavelet transform methods, and selected features

2021 ◽  
Vol 67 (1) ◽  
Author(s):  
Meilin Zhang ◽  
Qinghui Zhang ◽  
Junqiu Li ◽  
Jiale Xu ◽  
Jiawen Zheng
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Damage And Fracture ◽  
Mode Decomposition ◽  
Ae Signal ◽  
Ae Signals

AbstractThe nondestructive testing technology of generated acoustic emission (AE) signals for wood is of great significance for the evaluation of internal damages of wood. To achieve more accurate and adaptive evaluation, an AE signals classification method combining the empirical mode decomposition (EMD), discrete wavelet transform (DWT), and linear discriminant analysis (LDA) classifier is proposed. Five features (entropy, crest factor, pulse factor, margin factor, waveform factor) are selected for classification because they are more sensitive to the uncertainty, complexity, and non-linearity of AE signals generated during wood fracture. The three-point bending load damage experiment was implemented on sample wood of beech and Pinus sylvestris to generate original AE signals. Evaluation indexes (precision, accuracy, recall, F1-score) were adopted to assess the classification model. The results show that the ensemble classification accuracies of two tree species reach 94.58% and 90.58%, respectively. Moreover, compared with the results of the original AE signal, the accuracy of the AE signal processed by the methods proposed is increased by 27.68%. It indicates that the EMD and DWT signal processing methods and selected features improve the classification accuracy, and this automatic classification model has good AE signal recognition performance.

scholarly journals AE propagation velocity calculation for stiffness estimation in Pier Luigi Nervi’s concrete structures

2021 ◽  
Vol 8 (1) ◽  
pp. 109-118
Author(s):  
Erica Lenticchia ◽  
Amedeo Manuello Bertetto ◽  
Rosario Ceravolo
Keyword(s):  
Propagation Velocity ◽  
Concrete Structures ◽  
P Wave ◽  
Wave Propagation Velocity ◽  
Damage Level ◽  
Stiffness Characteristics ◽  
Vertical Bearing ◽  
The Relationship ◽  
Ae Signals ◽  
Elastic Characteristics

Abstract In the present paper, the acoustic emission (AE) device is used with an innovative approach, based on the calculation of P-wave propagation velocity (vp ), to detect the stiffness characteristics and the diffused damage of in-service old concrete structures. The paper presents the result of a recent testing campaign carried out on the slant pillars composing the vertical bearing structures designed by Pier Luigi Nervi in one of his most iconic buildings: the Hall B of Torino Esposizioni. In order to investigate the properties of these inclined pillars, localizations of artificial sources (hammer impacts), by the triangulation procedure, were performed on three different inclined elements characterized by stiffness discrepancies due to different causes: the casting procedures, executed in different stages, and the enlargement of the hall happened a few years later the beginning of the construction. In the present work, the relationship between the velocity of AE signals and the elastic characteristics (principally elastic modulus, E) is evaluated in order to discriminate the stiffness level of the slanted pillars. The procedure presented made it possible to develop an innovative investigation method able to estimate, by means of AE, the state of conservation and the elastic properties and the damage level of the monitored concrete and reinforced concrete structures.

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 Acoustic Emission Testing and Ib-Value Analysis of Ultraviolet Light-Irradiated Fiber Composites

2021 ◽  
Vol 11 (14) ◽  
pp. 6550
Author(s):  
Doyun Jung ◽  
Wonjin Na
Keyword(s):  
Acoustic Emission ◽  
Irradiation Time ◽  
Tensile Load ◽  
Failure Behavior ◽  
Value Analysis ◽  
Emission Testing ◽  
Final Failure ◽  
Acoustic Emission Testing ◽  
Woven Glass Fiber ◽  
Ae Signals

The failure behavior of composites under ultraviolet (UV) irradiation was investigated by acoustic emission (AE) testing and Ib-value analysis. AE signals were acquired from woven glass fiber/epoxy specimens tested under tensile load. Cracks initiated earlier in UV-irradiated specimens, with a higher crack growth rate in comparison to the pristine specimen. In the UV-degraded specimen, a serrated fracture surface appeared due to surface hardening and damaged interfaces. All specimens displayed a linearly decreasing trend in Ib-values with an increasing irradiation time, reaching the same value at final failure even when the starting values were different.

Acoustic Emission Monitoring of Leaks in Pipes for Transport of Liquid and Gaseous Media: A Model Experiment

2006 ◽  
Vol 13-14 ◽  
pp. 351-356 ◽  
Author(s):  
Andreas J. Brunner ◽  
Michel Barbezat
Keyword(s):  
Acoustic Emission ◽  
Model Experiment ◽  
Structural Integrity ◽  
Fiber Composite ◽  
Leak Testing ◽  
Pipe Segment ◽  
Potential Applications ◽  
Gaseous Media ◽  
The Media ◽  
Ae Signals

In order to explore potential applications for Active Fiber Composite (AFC) elements made from piezoelectric fibers for structural integrity monitoring, a model experiment for leak testing on pipe segments has been designed. A pipe segment made of aluminum with a diameter of 60 mm has been operated with gaseous (compressed air) and liquid media (water) for a range of operating pressures (between about 5 and 8 bar). Artificial leaks of various sizes (diameter) have been introduced. In the preliminary experiments presented here, commercial Acoustic Emission (AE) sensors have been used instead of the AFC elements. AE sensors mounted on waveguides in three different locations have monitored the flow of the media with and without leaks. AE signals and AE waveforms have been recorded and analysed for media flow with pressures ranging from about 5 to about 8 bar. The experiments to date show distinct differences in the FFT spectra depending on whether a leak is present or not.

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