scholarly journals Study on the frequency of acoustic emission signal during crystal growth of salicylic acid

2021 ◽  
Vol 10 (1) ◽  
pp. 596-604
Author(s):  
Xingjun Wang ◽  
Quanmin Xie ◽  
Ying Huang
Keyword(s):  
Particle Size ◽  
Acoustic Emission ◽  
Salicylic Acid ◽  
Vibration Frequency ◽  
Diffusion Theory ◽  
Elastic Vibration ◽  
Growth Unit ◽  
Time Frequency ◽  
Ae Signal ◽  
The Impact

Abstract Based on the results of the previous experiment, this article studied the acoustic emission (AE) signals released during the crystallization of salicylic acid to establish the relationship between the AE signal and the particle size. A tremendous amount of acoustic data was analyzed using time–frequency domain analysis methods in order to extract the valuable contents. Based on the diffusion theory, the vibratory model between the AE signal and the crystal particle size was established. This article mainly studies the process of small particles diffusing to the growth point by impact, adding to the lattice, and the crystal releases energy. The impact of the growth unit on particle aggregate is equivalent to a linear elastic vibration system with one end fixed and the other end free. The vibration frequency is 200–355 kHz when the particle size is between 600 and 1,100 µm. The calculated vibration frequency is in good agreement with the measured frequency.

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.

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.

scholarly journals A Time-Frequency Based Approach for Acoustic Emission Assessment of Sliding Wear

Lubricants ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 52
Author(s):  
Igor Rastegaev ◽  
Dmitry Merson ◽  
Inna Rastegaeva ◽  
Alexei Vinogradov
Keyword(s):  
Acoustic Emission ◽  
Sliding Friction ◽  
Monitoring And Control ◽  
Destructive Testing ◽  
Noise Sources ◽  
Processing Technologies ◽  
Time Frequency ◽  
On Line ◽  
Ae Signal ◽  
And Control

The acoustic emission method is one of few contemporary non-destructive testing techniques enabling continuous on-line health monitoring and control of tribological systems. However, the existence of multiple “pseudo”-acoustic emission (AE) and noise sources during friction, and their random occurrence poses serious challenges for researchers and practitioners when extracting “useful” information from the upcoming AE signal. These challenges and numerous uncertainties in signal classification prevent the unequivocal interpretation of results and hinder wider uptake of the AE technique despite its apparent advantages. Currently, the signal recording and processing technologies are booming, and new applications are born on this support. Specific tribology applications, therefore, call for developing new and tuning existing approaches to the online AE monitoring and analysis. In the present work, we critically analyze, compare and summarize the results of the application of several filtering techniques and AE signal classifiers in model tribological sliding friction systems allowing for the simulation of predominant wear mechanisms. Several effective schemes of AE data processing were identified through extensive comparative studies. Guidelines were provided for practical application, including the online monitoring and control of the systems with friction, characterizing the severity and timing of damage, on-line evaluation of wear as sliding contact tests and instrumented acceleration of tribological testing and cost reduction.

scholarly journals Failure mechanism and acoustic emission signal characteristics of coatings under the condition of impact indentation

2019 ◽  
Vol 38 (2019) ◽  
pp. 601-611
Author(s):  
Dong Tian-Shun ◽  
Wang Ran ◽  
Li Guo-Lu ◽  
Liu Ming
Keyword(s):  
Acoustic Emission ◽  
Surface Morphology ◽  
Failure Mechanism ◽  
Bonding Strength ◽  
Cross Sectional ◽  
Emission Signal ◽  
Signal Characteristics ◽  
Interface Cracking ◽  
Ae Signal ◽  
The Impact

AbstractIn this work, the substrate, NiCr coating, Al2O3 coating with NiCr undercoating and Al2O3 coating were tested by an impact indentation device equipped with an acoustic emission (AE) detection equipment. The surface morphology, dimension, cross-sectional image, 3D topography of indention and bonding strength of coatings were analyzed. The failure mechanism and AE signal characteristics of the coatings under impact were studied. The results demonstrate that the failure mode of NiCr coating was dominated by interface cracking, and that of Al2O3 coating is fracture and accompanied by a small amount of interface cracking, while Al2O3 coating with NiCr undercoating possesses common characteristics of the first two. The energy counting and wave voltage of AE signal were more sensitive to the bonding strength of coating in the impact process, which can be used to characterize the bonding strength of coating.

Feature Extraction of Acoustic Emission Signals for Rotor Fault Based on Daubechies Wavelet Analysis

2012 ◽  
Vol 424-425 ◽  
pp. 290-294
Author(s):  
Yang Yu ◽  
Ping Yang ◽  
Li Jian Yang
Keyword(s):  
Acoustic Emission ◽  
Daubechies Wavelet ◽  
Rotor Speed ◽  
High Scale ◽  
Time Frequency ◽  
Rotating Machine ◽  
Signal Change ◽  
Ae Signal ◽  
Non Stationary Signal ◽  
Modulus Maxima

The failure of rotating machine is often occurred by the rotor rubbing and the acoustic emission will be created by the rotor rubbing. The acoustic emission (AE) signal is the transient and non-stationary signal. Wavelet Transform has outstanding advantage revealing the character of this signal in time-frequency. With the increasing of the rotor speed, the noise increases a lot and the wavelet coefficients of AE signal change to high-scale. The db10 was applied in AE signal processing, and the modulus maxima method was applied to reconstruct of the signal. The result shows that the AE signal of rotor rubbing could be separated from noise by Daubechies Wavelet

Time-Frequency Characteristic and Recognition Technology of Acoustic Emission Generated from Rock Brittle Fracture

2013 ◽  
Vol 405-408 ◽  
pp. 116-119
Author(s):  
Jian Wei Liu ◽  
Xian Zhen Wu ◽  
Xiang Xin Liu
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Uniaxial Compression ◽  
Bp Neural Network ◽  
Rock Fracture ◽  
Strain Curve ◽  
Time Frequency ◽  
The Neural Network ◽  
Ae Signal ◽  
The Difference

To the instability of acoustic emission (AE) signal of rock fracture, the method about feature extraction and comprehensive recognition of those was came up with combining AE parameters, EMD and BP neural network. Through the acoustic emission experiment of different brittle rock under uniaxial compression, stress-strain curve and AE data were obtained; time-frequency characteristics of AE signal of rock samples were compared. Feature vectors, like AE parameters, and EMD energy entropy, was synthesized to BP neural network to distinguish different AE signal. The results show that evolution characteristic with stress or time of AE parameters of different rock which was under uniaxial compression exist similarities and differences. EMD and Welch spectrum can reflect the difference among spectrum, energy distribution of AE signal of different rock very well. With various characteristics of different rock acoustic emission, the neural network has good recognition effect.

scholarly journals Acoustic Emission-Based Condition Monitoring and Remaining Useful Life Prediction of Hydraulic Cylinder Rod Seals

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6012
Author(s):  
Jørgen F. Pedersen ◽  
Rune Schlanbusch ◽  
Thomas J. J. Meyer ◽  
Leo W. Caspers ◽  
Vignesh V. Shanbhag
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Hydraulic Cylinder ◽  
Remaining Useful Life ◽  
Test Rig ◽  
Operational Conditions ◽  
Hydraulic Cylinders ◽  
Ae Signal ◽  
Useful Life ◽  
The Impact

The foremost reason for unscheduled maintenance of hydraulic cylinders in industry is caused by wear of the hydraulic seals. Therefore, condition monitoring and subsequent estimation of remaining useful life (RUL) methods are highly sought after by the maintenance professionals. This study aimed at investigating the use of acoustic emission (AE) sensors to identify the early stages of external leakage initiation in hydraulic cylinders through run to failure studies (RTF) in a test rig. In this study, the impact of sensor location and rod speeds on the AE signal were investigated using both time- and frequency-based features. Furthermore, a frequency domain analysis was conducted to investigate the power spectral density (PSD) of the AE signal. An accelerated leakage initiation process was performed by creating longitudinal scratches on the piston rod. In addition, the effect on the AE signal from pausing the test rig for a prolonged duration during the RTF tests was investigated. From the extracted features of the AE signal, the root mean square (RMS) feature was observed to be a potent condition indicator (CI) to understand the leakage initiation. In this study, the AE signal showed a large drop in the RMS value caused by the pause in the RTF test operations. However, the RMS value at leakage initiation is seen to be a promising CI because it appears to be linearly scalable to operational conditions such as pressure and speed, with good accuracy, for predicting the leakage threshold.

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.

scholarly journals Experimental Investigation on Effective Distances of Acoustic Emission in Concrete Structures

2020 ◽  
Vol 10 (17) ◽  
pp. 6051 ◽  
Author(s):  
Tae-Min Oh ◽  
Min-Koan Kim ◽  
Jong-Won Lee ◽  
Hyunwoo Kim ◽  
Min-Jun Kim
Keyword(s):  
Acoustic Emission ◽  
Impact Energy ◽  
Concrete Structures ◽  
Propagation Distance ◽  
Monitoring Systems ◽  
Effective Distance ◽  
Destructive Testing ◽  
Length Width ◽  
Ae Signal ◽  
The Impact

As one of the non-destructive testing (NDT) methods, acoustic emission (AE) can be widely applied to the field of engineering and applied science owing to its advantageous characteristics. In particular, the AE method is effectively applied to monitor concrete structures in civil engineering. For this technology to be employed in a monitoring system, it is necessary to investigate the propagation characteristics of the AE in structures. Hence, this study investigates the characteristics of AE in concrete structures to evaluate the field applicability of AE monitoring systems. To achieve this goal, experiments employing an AE system are conducted for concrete structures 20 × 0.2 × 1.2 m in length, width, and height, respectively, to explore the AE parameters according to the impact energy. Among all AE parameters, absolute energy is determined to be most sensitive factor with respect to the impact energy. In addition, the attenuation effect of the AE wave is quantitatively evaluated according to the wave propagation distance. Moreover, the concept of effective distance is newly suggested based on the experimental results. The effective distance is shown to increase as the impact energy increases, although the increased effective distance is limited because the damaged AE signal is of high frequency. This study helps improve the field applicability of AE monitoring systems by suggesting suitable AE sensor spacing, which contributes to promote the practice of technology.

A Review on the Relationships Between Acoustic Emission, Friction and Wear in Mechanical Systems

2019 ◽  
Vol 72 (2) ◽  
Author(s):  
Peipei Feng ◽  
Pietro Borghesani ◽  
Wade A. Smith ◽  
Robert B. Randall ◽  
Zhongxiao Peng
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Friction And Wear ◽  
Theoretical Models ◽  
Experimental Investigations ◽  
Two Factors ◽  
Specific Contact ◽  
Comprehensive Picture ◽  
Ae Signal ◽  
The Impact

Abstract Acoustic emission (AE) techniques play a key role in machine condition monitoring and wear/fault diagnosis. Understanding the impact of friction and wear on the generation of AE signals is essential to building a reliable wear monitoring system. However, existing papers focus on only one or two factors in specific contact conditions. This paper aims at surveying studies related to both theoretical models and experimental investigations to produce a comprehensive picture of the relationship between tribological parameters (e.g., surface roughness, oil film thickness, and friction coefficient), operating parameters (e.g., sliding velocity and load), and AE signal characteristics (e.g., amplitude/energy, frequency, and event count). This result will provide guidance for the development of AE-based condition monitoring approaches and in particular for the establishment of AE-based wear assessment techniques.

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