scholarly journals Propagation Characteristics of Acoustic Emission Signals in Stiffened Cylindrical Shells Based on the Multipath Propagation Model

2021 ◽  
Vol 11 (24) ◽  
pp. 11722
Author(s):  
Cong Han ◽  
Tong Liu ◽  
Zhenhuan Wu ◽  
Guoan Yang
Keyword(s):  
Cylindrical Shell ◽  
Acoustic Emission ◽  
Excitation Frequency ◽  
Propagation Model ◽  
Detection Accuracy ◽  
Propagation Characteristics ◽  
Reflection And Transmission ◽  
Scattering Coefficients ◽  
Ae Signal ◽  
Ae Signals

A stiffener attached to a cylindrical shell strongly interferes with the propagation of the acoustic emission (AE) signal from the fault source and reduces the fault detection accuracy. The interaction of AE signals with the stiffener on the cylindrical shell is thoroughly investigated in this paper. Based on the proposed model of the AE signal propagating inside the cylindrical shell with a stiffener, the installation constraints for the sensor are derived, resulting in the separation of the direct signal, the stiffener scattering signal, and other signals in the time domain. On this basis, combinations of the excitation frequency and the stiffener height are simulated, and the reflection and transmission of the AE signal in each case are quantitatively characterized by the scattering coefficients. The results indicate that there is a “T-shaped” transformation of the signal at the stiffener, which evolves into a variety of other modes. Moreover, the reflection and transmission coefficients of the incident AE signal are displayed as a function of the excitation frequency and the height of the stiffener. In addition, the accuracy of the scattering coefficients obtained from the numerical simulations is verified by experiments, and a good consistency between simulation results and experiment results is presented. This work illustrates the propagation characteristics of AE signals in a cylindrical shell with a stiffener, which can be used as guidance for optimizing the spatial arrangement of sensors in AE monitoring.

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.

Experiment on Surface Roughness and Acoustic Emission Signal in Infeed Period of Cylindrical Plunge Grinding

2013 ◽  
Vol 690-693 ◽  
pp. 2442-2445 ◽  
Author(s):  
Hao Lin Li ◽  
Hao Yang Cao ◽  
Chen Jiang
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Feed Rate ◽  
Grinding Process ◽  
Experiment Research ◽  
Plunge Grinding ◽  
Emission Signal ◽  
Ae Signal ◽  
Ae Signals

This work presents an experiment research on Acoustic emission (AE) signal and the surface roughness of cylindrical plunge grinding with the different infeed time. The changed infeed time of grinding process is researched as an important parameter to compare AE signals and surface roughnesses with the different infeed time in the grinding process. The experiment results show the AE signal is increased by the increased feed rate. In the infeed period of the grinding process, the surface roughness is increased at first, and then is decreased.

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.

Reciprocating compressor valve damage estimation under varying speeds through the acoustic emission technique

2019 ◽  
Vol 10 (5) ◽  
pp. 621-633
Author(s):  
Hoi-Yin Sim ◽  
Rahizar Ramli ◽  
Ahmad Saifizul
Keyword(s):  
Acoustic Emission ◽  
Regression Analysis ◽  
Flow Rate ◽  
Reciprocating Compressor ◽  
Damage Estimation ◽  
Content Type ◽  
Air Compressor ◽  
Ae Signal ◽  
Valve Opening ◽  
Ae Signals

Purpose The purpose of this paper is to examine the effect of reciprocating compressor speeds and valve conditions on the roor-mean-square (RMS) value of burst acoustic emission (AE) signals associated with the physical motion of valves. The study attempts to explore the potential of AE signal in the estimation of valve damage under varying compressor speeds. Design/methodology/approach This study involves the acquisition of AE signal, valve flow rate, pressure and temperature at the suction valve of an air compressor with speed varrying from 450 to 800 rpm. The AE signals correspond to one compressor cycle obtained from two simulated valve damage conditions, namely, the single leak and double leak conditions are compared to those of the normal valve plate. To examine the effects of valve conditions and speeds on AE RMS values, two-way analysis of variance (ANOVA) is conducted. Finally, regression analysis is performed to investigate the relationship of AE RMS with the speed and valve flow rate for different valve conditions. Findings The results showed that AE RMS values computed from suction valve opening (SVO), suction valve closing (SVC) and discharge valve opening (DVO) events are significantly affected by both valve conditions and speeds. The AE RMS value computed from SVO event showed high linear correlation with speed compared to SVC and DVO events for all valve damage conditions. As this study is conducted at a compressor running at freeload, increasing speed of compressor also results in the increment of flow rate. Thus, the valve flow rate can also be empirically derived from the AE RMS value through the regression method, enabling a better estimation of valve damages. Research limitations/implications The experimental test rig of this study is confined to a small pressure ratio range of 1.38–2.03 (free-loading condition). Besides, the air compressor is assumed to be operated at a constant speed. Originality/value This study employed the statistical methods namely the ANOVA and regression analysis for valve damage estimation at varying compressor speeds. It can enable a plant personnel to make a better prediction on the loss of compressor efficiency and help them to justify the time for valve replacement in future.

scholarly journals Friction and Wear Monitoring Methods for Journal Bearings of Geared Turbofans Based on Acoustic Emission Signals and Machine Learning

Lubricants ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 29 ◽  
Author(s):  
Noushin Mokhtari ◽  
Jonathan Gerald Pelham ◽  
Sebastian Nowoisky ◽  
José-Luis Bote-Garcia ◽  
Clemens Gühmann
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Friction And Wear ◽  
Journal Bearing ◽  
Journal Bearings ◽  
Wear Volume ◽  
Time Frequency ◽  
Ae Signal ◽  
Ae Signals

In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume.

Acoustic Emission During Athermal Martensitic Transformation in Steels

1990 ◽  
Vol 112 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Xiangying Liu ◽  
Elijah Kannatey-Asibu
Keyword(s):  
Free Energy ◽  
Acoustic Emission ◽  
Martensitic Transformation ◽  
Signal Intensity ◽  
Temperature Derivative ◽  
Process Characteristics ◽  
Ae Signal ◽  
The Relationship ◽  
Ae Signals ◽  
Start Temperature

A relationship developed earlier between acoustic emission signals and the process of athermal martensitic transformation based on the free energy associated with the process is extended and verified experimentally. The relationship is found to model the process characteristics very well. The intensity of AE signal generated during transformation was found to be proportional to the temperature derivative of the fraction of martensite, the cooling rate, and volume of specimen. The AE signal was also found to be related to the carbon content of the steel. During transformation, the signal intensity was found to increase to a peak, and then tail off near the end of the transformation. Values of the martensite start temperature obtained from plots of the total RMS squared AE signals were also found to correlate well with values from the literature.

scholarly journals Damage evolution in wood – pattern recognition based on acoustic emission (AE) frequency spectra

Holzforschung ◽  
2015 ◽  
Vol 69 (3) ◽  
pp. 357-365 ◽  
Author(s):  
Franziska Baensch ◽  
Markus G.R. Sause ◽  
Andreas J. Brunner ◽  
Peter Niemz
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Cluster Formation ◽  
Low Frequency ◽  
Tensile Tests ◽  
Most Probable Number ◽  
Frequency Spectra ◽  
Probable Number ◽  
Ae Signal ◽  
Ae Signals

Abstract Tensile tests on miniature spruce specimens have been performed by means of acoustic emission (AE) analysis. Stress was applied perpendicular (radial direction) and parallel to the grain. Nine features were selected from the AE frequency spectra. The signals were classified by means of an unsupervised pattern recognition approach, and natural classes of AE signals were identified based on the selected features. The algorithm calculates the numerically best partition based on subset combinations of the features provided for the analysis and leads to the most significant partition including the respective feature combination and the most probable number of clusters. For both specimen types investigated, the pattern recognition technique indicates two AE signal clusters. Cluster A comprises AE signals with a relatively high share of low-frequency components, and the opposite is true for cluster B. It is hypothesized that the signature of rapid and slow crack growths might be the origin for this cluster formation.

INFLUENCE OF ACOUSTIC-ELECTRONIC CHANNEL ON ACOUSTIC EMISSION HIT PARAMETERS

2020 ◽  
pp. 14-22
Author(s):  
A. A. Sazonov ◽  
V. I. Shelobkov ◽  
V. I. Ivanov
Keyword(s):  
Acoustic Emission ◽  
Significant Influence ◽  
Signal Propagation ◽  
Propagation Channel ◽  
Object State ◽  
Signal Parameters ◽  
Ae Signal ◽  
Electronic Channel ◽  
Ae Signals ◽  
Electronic Channels

The paper deals with the influence of acoustic emission (AE) signal propagation channel on the parameters of these signals, which are used to judge the object state. It is shown that the acoustic channel, which includes the testing object and the acoustic emission transducer, has a significant influence on the parameters of AE signals. This must be taken into account, both in the interpretation of signals and in the calibration of acoustic emission transducers. Specific examples of degradation of AE signal parameters during the passage of acoustic-electronic channels are shown.

Real-Time Monitoring of Degradation of Zirconia Ceramic by Acoustic Emission

2013 ◽  
Vol 433-435 ◽  
pp. 816-820 ◽  
Author(s):  
Kai Ge Wu ◽  
Chan Yang Choe ◽  
Seung Mi Lee ◽  
Ji Ae Yoo ◽  
Chang Yong Hyun ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Low Temperature ◽  
Time Domain ◽  
Zirconia Ceramic ◽  
X Ray Diffraction ◽  
Zirconia Ceramics ◽  
X Ray ◽  
Ae Signal ◽  
Three Stages ◽  
Ae Signals

Acoustic emission (AE) technique was employed to monitor the low temperature degradation (LTD) behavior of a zirconia ceramic, which was carried out in air at 200°C. AE signals, typical waveform of which exhibited as pulse-like type, were detected during LTD and increased with the duration time of LTD. The AE signal of accumulative counts number and amplitude in time domain exhibited a roughly sigmoid variation including three stages, which reflect approximately the evolution of LTD. The AE findings were supported by X-ray diffraction (XRD) examination. It was suggested that AE technique can be used to monitor the mechanism of LTD of zirconia ceramics.

Using Acoustic Emission and Vibration Detection to Identify the Rotor-Stator Rubbing

2007 ◽  
Author(s):  
Zhansheng Liu ◽  
Xiaowei Wang ◽  
Wei Dou
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Vibration Analysis ◽  
Turbine Rotor ◽  
Vibration Monitoring ◽  
Rotating Machine ◽  
Vibration Detection ◽  
Ae Signal ◽  
Many Sources ◽  
Ae Signals

Vibration monitoring of rotating machines is probably the most established diagnostic method. The application of acoustic emission (AE) for rotating machine fault diagnosis is gained as a complementary tool; however, limitations in the successful application of the AE technique have been partly due to the difficulty in processing, interpreting and classifying the acquired data. The experimental investigation reported in this paper is centred on the application of the AE technique for identifying the seal rubbing on the rotor rig. An experimental test rig was designed to simulate the 200MW gas turbine rotor shafts. On the rig different degrees rubbing-impact on the seal is performed. The AE transducer and the vibration acceleration transducer are set on the bearing block. Comparisons between AE and vibration analysis over a range of speed and different degrees rubbing-impact are presented. In fact there are so many sources of AE that the successful identification of rubbing-impact signal is very important. Account for the characteristics of acoustic emission signals the wavelet transform is employed to analyze the AE signal. The wavelet transform can decompose the AE signals in time and wavelet scale domains, and catch the differences in these waves. It enables to distinguish the rubbing-impact from other sources. It is concluded that AE offers earlier fault detection and improved identification capabilities than vibration analysis, allowing the user to monitor the rubbing-impact degrees of the rotor system, unachievable with vibration analysis.

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