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.

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 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 Condition Monitoring of the Coroning Process

2009 ◽  
Author(s):  
Juil Yum ◽  
Amir Kamouneh ◽  
Wencai Wang ◽  
Elijah Kannatey-Asibu
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Visual Observation ◽  
Tool Condition Monitoring ◽  
Selection Methods ◽  
Tool Failure ◽  
Minimum Error ◽  
Tool Condition ◽  
Frequency Components ◽  
Ae Signal

Acoustic emission (AE) is introduced for tool condition monitoring during the coroning process. The frequency components of the AE signal were used as features for classification. Two different feature selection methods were investigated, namely visual observation and the class mean scatter criterion. The minimum error rate Bayesian rule was used to distinguish between two extreme tool conditions. Although the features from visual observation could result in 100% classification, features based on the class mean scatter criterion showed excellent monitoring capability of tool failure when fewer features were used.

scholarly journals Experimental studies and theoretical models for concrete columns confined with FRP composites: a review

2019 ◽  
Vol 16 (4) ◽  
pp. 509-525
Author(s):  
Abdurra’uf M. Gora ◽  
Jayaprakash Jaganathan ◽  
M.P. Anwar ◽  
H.Y. Leung
Keyword(s):  
Concrete Columns ◽  
Theoretical Models ◽  
Experimental Investigations ◽  
Future Research ◽  
Content Type ◽  
Compressive Behaviour ◽  
Frp Composites ◽  
Frp Reinforcement ◽  
Frp Confinement ◽  
The Impact

Purpose Advanced fibre-reinforced polymer (FRP) composites have been increasingly used over the past two decades for strengthening, upgrading and restoring degraded civil engineering infrastructure. Substantial experimental investigations have been conducted in recent years to understand the compressive behaviour of FRP-confined concrete columns. A considerable number of confinement models to predict the compressive behaviour of FRP-strengthened concrete columns have been developed from the results of these experimental investigations. The purpose of this paper is to present a comprehensive review of experimental investigations and theoretical models of circular and non-circular concrete columns confined with FRP reinforcement. Design/methodology/approach The paper reviews previous experimental test results on circular and non-circular concrete columns confined with FRP reinforcement under concentric and eccentric loading conditions and highlights the behaviour and mechanics of FRP confinement in these columns. The paper also reviews existing confinement models for concrete columns confined with FRP composites in both circular and non-circular sections. Findings This paper demonstrates that the performance and effectiveness of FRP confinement in concrete columns have been extensively investigated and proven effective in enhancing the structural performance and ductility of strengthened columns. The strength and ductility enhancement depend on the number of FRP layers, concrete compressive strength, corner radius for non-circular columns and intensity of load eccentricity for eccentrically loaded columns. The impact of existing theoretical models and directions for future research are also presented. Originality/value Potential researchers will gain insight into existing experimental and theoretical studies and future research directions.

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.

scholarly journals Nondestructive Detection of Valves Using Acoustic Emission Technique

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jin Yan ◽  
Yang Heng-hu ◽  
Yang Hong ◽  
Zhang Feng ◽  
Liu Zhen ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Detection System ◽  
Theoretical Models ◽  
Acoustic Emission Technique ◽  
Nondestructive Detection ◽  
Basic Composition ◽  
Measuring Devices ◽  
Cavitation Detection ◽  
Ae Signals

The applications of acoustic emission (AE) technique in detection of valves are presented in this review, and the theoretical models and experimental results of nondestructive detection of valves using AE are provided. The generation of AE signals and the basic composition of AE detection system are briefly explained. The applications of AE technique in valves are focused on condition monitoring, failure, cavitation detection, and the development of portable measuring devices. All results prove that the AE technique works well in the detection of valves.

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 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.

Gas Turbine Condition Monitoring Using Acoustic Emission Signals

2019 ◽  
Vol 2 (3) ◽  
Author(s):  
S. Shahkar ◽  
K. Khorasani
Keyword(s):  
Acoustic Emission ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Gas Turbines ◽  
Acoustic Emissions ◽  
Time Instant ◽  
Rotary Machinery ◽  
Ae Signal ◽  
Reliable Classification ◽  
Ae Signals

Acoustic emission (AE) signals are recognized as complementary measures for detecting incipient faults and condition monitoring in rotary machinery due to their containment of sources of potential fault energy. However, determining the potential sources of faults cannot be easily realized due to the non-stationarity of AE signals. Available techniques that are capable of evoking instantaneous characteristics of a particular AE signal cannot optimally perform in a sense that there is no guarantee that these characteristics (hereinafter referred to as the “features”) remain constant when another AE signal is obtained from the system, albeit operating under the same machine condition at a different time instant. This paper provides a theoretical framework for developing a highly reliable classification and detection methodology for gas turbine condition monitoring based on AE signals. Mathematical results obtained in this paper are evaluated and validated by using actual gas turbines that are operating in power generating plants, to demonstrate the practicality and simplicity of our methodologies. Emphasis is given to acoustic emissions of similar brand and sized gas turbine turbomachinery under different health conditions and/or aging characteristics.

scholarly journals Influence of Frequency and Distance on Acoustic Emission Velocity Propagating in Various Dielectrics

2020 ◽  
Vol 10 (9) ◽  
pp. 3305
Author(s):  
Daria Wotzka
Keyword(s):  
Acoustic Emission ◽  
Nonlinear Regression ◽  
Modulation Frequency ◽  
Partial Discharges ◽  
Time Of Arrival ◽  
Dielectric Liquids ◽  
Signal Parameters ◽  
Two Factors ◽  
Ae Signal

The results obtained for the measurements of acoustic emission (AE) signal parameters emitted in three dielectric liquids are presented in this paper. In particular, the velocity of AE wave was calculated based on the time of arrival of the AE signal. A frequency modulated signal was generated by a piezoelectric transducer and measured at various distances by a hydrophone. The changes in velocity values at particular distances and for different frequencies were investigated. The analyses include the determination of the dependency of the velocity values from the distance between the communicating devices. A nonlinear regression model was calculated, and the differences between AE velocities propagating in the considered dielectrics were determined. Similarly, the influence of modulation frequency on the AE velocity was determined using nonlinear regression. Based on the calculation data, it can clearly be stated that the velocity of AE wave depends significantly on the frequency and distance at which it is registered. These two factors may have an important influence on the localization of partial discharges (PD) occurring in these types of dielectric liquids.

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