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.

Study on Failure Mechanisms of Composite Materials Based on HHT

2013 ◽  
Vol 477-478 ◽  
pp. 30-33
Author(s):  
Wen Qin Han ◽  
Jin Yu Zhou
Keyword(s):  
Composite Materials ◽  
Failure Mechanisms ◽  
Acoustic Emissions ◽  
Deep Understanding ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Composite Failure ◽  
Mode Decomposition ◽  
Materials Failure ◽  
Twill Weave

In order to get a deep understanding of composite failure mechanisms, the new advanced signal processing methodologies are established for the analysis of acoustic emission (AE) data obtained from the quasi-static tension test of 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 Hilbert-Huang Transform (HHT) of each IMF component was performed, it was shown that the frequency distribution of IMF component in time-scale could be directly related to composite materials failure mechanisms.

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%.

Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

2010 ◽  
Vol 36 ◽  
pp. 68-74
Author(s):  
Chuan Jun Liao ◽  
Shuang Fu Suo ◽  
Wei Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Mechanical Seal ◽  
Mechanical Seals ◽  
Feature Extraction Method ◽  
Different Types ◽  
Ae Signal ◽  
Feature Information ◽  
Ae Signals

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

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.

scholarly journals Internal Leakage Predicition of Hydraulic Spool valves Based on Acoustic Emission Technology

2021 ◽  
Vol 2113 (1) ◽  
pp. 012016
Author(s):  
Fei Song ◽  
Likun Peng ◽  
Jia Chen ◽  
Benmeng Wang
Keyword(s):  
Acoustic Emission ◽  
High Frequency ◽  
Peak Frequency ◽  
Leakage Rate ◽  
High Frequency Band ◽  
Signal Characteristics ◽  
Spool Valves ◽  
The Relationship ◽  
Ae Signals ◽  
Acoustic Emission Technology

Abstract In order to realize the nondestructive testing (NDT) of the internal leakage fault of hydraulic spool valves, the internal leakage rate must be predicted by AE (acoustic emission) technology. An AE experimental platform of internal leakage of hydraulic spool valves is built to study the characteristics of AE signals of internal leakage and the relationship between AE signals and leakage rates. The research results show the AE signals present a wideband characteristic. The main frequencies are concentrated in 30~50 kHz and the peak frequency is around 40 kHz. When the leakage rate is large, there are significant signal characteristics appearing in the high frequency band of 75~100 kHz. The exponent of the root mean square(RMS) of AE signals is positively correlated with the exponent of the leakage rate only if the leakage rate is greater than 2~3 mL/min. This find could be used to predict the internal leakage rate of hydraulic spool valves.

Evaluation of Defect in Compostie Using Acousto-Ultrasonic Technique

2006 ◽  
Vol 326-328 ◽  
pp. 1267-1270
Author(s):  
Min Rae Lee ◽  
Joon Hyun Lee
Keyword(s):  
Acoustic Emission ◽  
Composite Materials ◽  
Acoustic Emissions ◽  
Diagnostic Information ◽  
Ultrasonic Technique ◽  
Fiber Reinforced Composite ◽  
Reinforced Composite ◽  
First Results ◽  
Material Sample ◽  
Internal Sources

This paper is focused on the capability of the Acousto-Ultrasonic (AU) technique and the non-contact technique to provide diagnostic information useful to detect defect in composite. An acousto-ultrasonics (AU) is to simulate stress wave that resemble acoustic emission waves but without disrupting the material. One launched inside the material sample, the wave are modified by stochastic processes like those that affect spontaneous acoustic emissions from internal sources during stressing, deformation, etc. Moreover, acousto-uloasonic waves are launched periodically at predetermined times and with predetermined reparation rates. A fiber reinforced composite materials should be inspected in fabrication process in order to enhance quality by prevent defects such as delamination and void. In conventional ultrasonic technique for the evaluation of FRP, the transducer should be contacted on FRP. Therefore, in this study, advanced conventional contacting method (AU) and non-contact technique using air-coupled transducer can make contact and noncontacting ultrasonic technique available in evaluation of FRP. This paper demonstrates first results using an acousto-ultrasonic technique.

Study of Oil Starvation in Journal Bearing Using Acoustic Emission and Vibration Measurement Techniques

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Surojit Poddar ◽  
N. Tandon
Keyword(s):  
Acoustic Emission ◽  
Journal Bearing ◽  
Measurement Techniques ◽  
Peak Frequency ◽  
Vibration Measurement ◽  
Frequency Range ◽  
Frequency Shifts ◽  
Deformation And Fracture ◽  
Asperity Contacts ◽  
Ae Signals

Abstract This present article evaluates the state of starvation in a journal bearing using acoustic emission (AE) and vibration measurement techniques. A journal bearing requires a constant supply of oil in an adequate amount to develop a hydrodynamic film, thick enough to separate the surfaces and avoid asperity contacts. On a microscopic level, the surface interaction under starved lubrication results in deformation and fracture of asperities. This causes a proportionate increase in AE and vibration. The AE activities resulting from asperities interaction have significant energy in the frequency range of 100–400 kHz with peak frequencies in the range of 224–283 kHz. Further, the peak frequency shifts from the higher to lower side as the asperity interaction transits from the elastic to plastic contact. This information derived from the spectral analysis of AE signals can be used to develop condition monitoring parameters to proactively control the lubrication and prevent bearing failure.

Cluster Analysis of Acoustic Emission Signals During Carbon Steel Pitting Corrosion Process

2014 ◽  
Author(s):  
Zhongzheng Zhang ◽  
Cheng Ye ◽  
Jun Jiang
Keyword(s):  
Cluster Analysis ◽  
Acoustic Emission ◽  
Carbon Steel ◽  
Ferric Chloride ◽  
Pitting Corrosion ◽  
Chloride Solution ◽  
Ferric Chloride Solution ◽  
Corrosion Process ◽  
Different Types ◽  
Ae Signals

In order to study acoustic emission (AE) signals characteristics of pitting corrosion on carbon steel, Pitting corrosion process on carbon steel in 6% ferric chloride solution was monitored by AE technology. K-mean cluster algorithm was used to classify the monitored AE signals, in which the duration, counts, amplitude, absolute energy and peak frequency were analyzed as the AE signals characteristics, and different types AE sources were identified. The results showed that there were mainly three type AE sources during carbon steel pitting corrosion process in ferric chloride solution, and the different types AE sources could be classified by cluster analysis. The research results have some certain significance for AE monitoring of pitting corrosion on carbon steel.

The Research on Leak Detection Technology of Natural Gas Pipeline Based on EMD

2014 ◽  
Vol 494-495 ◽  
pp. 793-796 ◽  
Author(s):  
Chuan Jiang Li ◽  
Jia Pan Zhang ◽  
Zi Qiang Zhang ◽  
Ju Li Hu ◽  
Yi Li
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Leakage Detection ◽  
Intrinsic Mode Functions ◽  
Emission Signal ◽  
Natural Gas Pipeline ◽  
Detection Technology ◽  
Mode Decomposition ◽  
Leakage Characteristics ◽  
Signal Processing Methods

The acoustic emission signal of pipeline leakage is characterized by nonlinear and non-stationary. It is not feasible to extract the leakage feature signal in traditional signal processing methods. The leak locations can be detected by employing the improved empirical mode decomposition (EMD) to decompose the acoustic emission signal into several intrinsic mode functions (IMF), choosing IMFs containing leakage characteristics to be reconstructed, and doing correlation analysis. Experimental results show that the positioning accuracy of leakage detection is improved obviously.

Pipeline Leak Detection Based on Acoustic Emission Using Empirical Mode Decomposition Method

2008 ◽  
Author(s):  
Yibo Li ◽  
Junlin Li ◽  
Liying Sun ◽  
Shijiu Jin ◽  
Shenghua Han
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
Signal Analysis ◽  
Leak Detection ◽  
Detection Sensitivity ◽  
Intrinsic Mode Functions ◽  
Location Accuracy ◽  
Noise Interference ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

Corrosion in pipeline is a significant problem in the oil industry and there is also much interest in reducing leak due to corrosion. Correlation techniques are widely used in leak detection, and these have been extremely effective when attempting to locate leaks in metal pipes. Acoustic emission is a new non-destructive pipeline inspection technology which can be used to monitor crucial part of pipelines and detect pipe corrosion or leak in real time. However, AE signals causing by corrosion and leak are liable to noise interference on field. Aiming at solving the noise interference problems and increase the detection sensitivity and location accuracy of the leak, advanced signal analysis method based on Empirical Mode Decomposition were researched. Empirical Mode Decomposition is a great breakthrough in non-stable signal analysis and it decomposes the signals into a sum of finite intrinsic mode functions (IMF), which have real physical meaning. In the experiment, the leak signals from a 30 m pipeline were decomposed into 9 intrinsic mode functions by EMD, among which some IMF components containing typical AE characteristic can be selected to reconstruct the signal, and thus intrinsic characteristic of leak signal could be extracted and noise interference would be eliminated. Location accuracy of the leaking hole calculated with the reconstructed signals based on EMD algorithm was increased 64%.

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