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.

scholarly journals Early Detection and Identification of Fatigue Damage in Thrust Ball Bearings by an Acoustic Emission Technique

Lubricants ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 37 ◽  
Author(s):  
Alan Hase
Keyword(s):  
Acoustic Emission ◽  
Fatigue Damage ◽  
Signal Analysis ◽  
Vibration Analysis ◽  
Rolling Bearings ◽  
Frequency Spectra ◽  
New Findings ◽  
Ae Signal ◽  
Wear Modes ◽  
Ae Signals

As rolling bearings are widely used in various machines, there is a strong need to detect any problems as early as possible. Although vibration analysis is commonly used in the diagnosis of rolling bearings, it is possible that the failure of such bearings might be detected earlier by an acoustic emission (AE) technique. Methods for detecting potential fatigue damage in a thrust ball bearing by AE signal analysis and by vibration analysis were compared. For the AE signal analysis, the maximum amplitude and the frequency spectrum were used to detect and identify fatigue damage in the bearing. Features of AE signals detected when a defect was artificially formed on the raceway surface of a bearing by using a Vickers hardness tester were also examined. The AE technique detected initial cracks due to fatigue damage earlier than the vibration technique. Additionally, AE signals were always detected during bearing fatigue tests, but the AE signals detected during the running-in process, crack initiation, crack propagation, and flaking all contained different frequency components. Furthermore, the correlation map between the frequency spectra of AE signals and deformation and fracture phenomena (friction and wear modes) was updated by adding the new findings of this study.

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.

Research on Acoustic Emission Signals Characteristics of Pitting Corrosion on 304 Stainless Steel

2013 ◽  
Author(s):  
Zhongzheng Zhang ◽  
Hua Liang ◽  
Cheng Ye ◽  
Wensheng Cai ◽  
Jun Jiang ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Pitting Corrosion ◽  
High Frequency ◽  
Low Frequency ◽  
Ferric Chloride Solution ◽  
304 Stainless Steel ◽  
Flexural Wave ◽  
Higher Temperature ◽  
Ae Signals

In order to study acoustic emission (AE) signals waveform characteristics of pitting corrosion on 304 stainless steel under higher temperature than lower one, Pitting corrosion process on 304 stainless steel in 6% ferric chloride solution at 70°C was monitored by AE technology. Wavelet transform and mode acoustic emission technology were combined to deal with recorded AE signals, and micromorphologic observation was performed for further verification. The results showed that signal waveform was mainly composed of low-frequency (<100KHz) flexural wave with larger amplitude & energy and high-frequency (>100KHz) expansion wave with lesser amplitude & energy. The research results have some certain significance for AE monitoring of pitting corrosion on 304 stainless steel.

Fatigue behavior and damage analysis by the acoustic emission technique of cross-ply laminates under tensile loading

2017 ◽  
Vol 13 (2) ◽  
pp. 150-164
Author(s):  
Imen Ben Ammar ◽  
Abderrahim El Mahi ◽  
Chafik Karra ◽  
Rachid El Guerjouma ◽  
Mohamed Haddar
Keyword(s):  
Acoustic Emission ◽  
Mechanical Behavior ◽  
Fatigue Behavior ◽  
Glass Fibers ◽  
Tensile Tests ◽  
Hybrid Fibers ◽  
Content Type ◽  
Laminated Materials ◽  
Ae Signals ◽  
Damage Investigation

Purpose The purpose of this paper is to study the mechanical behavior in fatigue tensile mode of different cross-ply laminates constituted of unidirectional carbon fibers, hybrid fibers and glass fibers in an epoxy matrix; and to identify and characterize the local damage in the laminated materials with the use of the acoustic emission (AE) technique. Design/methodology/approach The tests in the fatigue mode permitted the determination of the effect of the stacking sequences, thickness of 90° oriented layers and reinforcement types on the fatigue mechanical behavior of the laminated materials. The damage investigation in those materials is reached with the analysis of AE signals collected from fatigue tensile tests. Findings The results show the effects of reinforcement type, stacking sequences and thicknesses ratio of 90° and 0° layers on the mechanical behavior. A cluster analysis of AE data is achieved and the resulting clusters are correlated with the damage mechanism of specimens under loading tests. Originality/value The analysis of AE signals collected from tensile tests of the fatigue failure load allows the damage investigation in different types of cross-ply laminates which are differentiated by the reinforcement type, stacking sequences and thicknesses ratio of 90° and 0° layers.

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.

Sign in / Sign up

Export Citation Format

Share Document