In-Situ Wear Monitoring of Slider and Disk Using Acoustic Emission

2000 ◽  
Vol 123 (1) ◽  
pp. 175-180 ◽  
Author(s):  
Kaoru Matsuoka ◽  
Koji Taniguchi ◽  
Masaru Nakakita
Keyword(s):  
Acoustic Emission ◽  
Wear Volume ◽  
Disk Interface ◽  
Wear Process ◽  
Slip Ring ◽  
Ae Signal ◽  
Wear Monitoring ◽  
Transducer Output ◽  
Ae Signals

The methodology has been developed for both the evaluation and analysis of slider/disk interface phenomena. We have been studying the direct relationships between the acoustic emission (AE) signal and wear of materials. The power in the AE signal is directly related to the power required for material removal in the wear process. This technique has been successfully applied to monitoring the wear of the tri-pad contact slider and the disk. The AE transducers were directly mounted onto both the arm with the slider and the disk in order to measure the slider/disk contact behavior. The AE transducer output from the disk was transmitted by the slip ring and the brush. The predicted wear of the slider and the disk based on the AE signals were computed from the relationship mentioned above. The measured wear of the slider and the disk were obtained by atomic force microscopy (AFM) and an optical surface analyzer (OSA) respectively. According to the experimental results, the predicted wear of both the slider and the disk using AE signals agreed with the wear which was measured. Therefore, wear can be estimated and monitored indirectly in-situ using the AE signals without direct measurements of the wear volume.

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 Identification of the Wear Process of a Silver-Plating Layer by Dual Acoustic Emission Sensing

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 737
Author(s):  
Alan Hase ◽  
Yousuke Sato ◽  
Keisuke Shinohara ◽  
Kentaro Arai
Keyword(s):  
Acoustic Emission ◽  
Copper Substrate ◽  
Frictional Resistance ◽  
Phase Iii ◽  
Wear Process ◽  
Acoustic Emission Sensing ◽  
Ae Signal ◽  
Measurement And Evaluation ◽  
Ae Signals ◽  
Plating Layer

A method based on acoustic emission (AE) sensing in which two AE sensors are used to measure the tribological characteristics of two interacting friction materials simultaneously in real time was assessed for the in situ measurement and evaluation of the wear process of silver plating. AE sensors were attached to a silver-plated pin and a silver-plated plate, and the two AE signals were measured simultaneously on a pin-on-plate-type reciprocating sliding tester. The resulting changes in the AE signal could be classified into three phases. Surface observations and energy-dispersive X-ray spectroscopy analyses showed that the wear of the silver-plating layer progressed in Phase I, the nickel intermediate layer was exposed and wear of the nickel progressed in Phase II, and the contact electrical resistance increased and the copper substrate was exposed in Phase III. In summary, the wear process of a silver-plating layer, which cannot be identified from the changes in the frictional resistance or the contact electric resistance, can be detected from changes in the dual AE signals. Furthermore, changes in the wear state of both the pin and plate specimens can be identified from differences in the amplitudes of the AE signals and the timing of their detection.

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.

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.

The Design and Development of an Evaluation System for Redox Characteristics of Anode Supported SOFCs Using In-Situ Acoustic Emission and Electrochemical Technique

2012 ◽  
Author(s):  
S. Hashimoto ◽  
H. Watanabe ◽  
T. Sakamoto ◽  
T. Kawada ◽  
K. Yashiro ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Evaluation System ◽  
Voltage Drop ◽  
Cell Voltage ◽  
Test System ◽  
Electrochemical Technique ◽  
Gas Concentration ◽  
Ae Signal ◽  
Cell Test

In this study, a redox evaluation system for anode supported SOFCs using in-situ acoustic emission (AE) and electrochemical technique has been developed. The system consists of a gas blending unit, moisture controlling unit, AE cell evaluation probe, gas cooling exhaust, electrochemical cell test system and AE signal measurement system. The anode supported coin cells, which have the same thickness dimension as practical SOFCs have, can be evaluated under temperature and atmosphere controlled conditions. The oxygen partial pressure in the anodic atmospheres can be gradually controlled from air to reducing atmosphere using the gas blending unit which is connected to 6 gas cylinders. Humidity in the anodic atmospheres can be controlled by moisture controlling unit which consists of 2 bubblers form 0.86% (5°C saturation) up to 80% (94°C saturation). Redox process of the anode can be simulated in this system by controlled three oxidation modes, i.e. O2 gas oxidation, steam oxidation and electrochemical oxidation, which correspond to actual troubles, i.e. gas leakage, degradation of downstream and fuel depletion, respectively. An AE transducer can monitor the cell condition via an inner tube for a guide of exhaust from the cathode. Redox cell test for the anode supported coin cell has been examined at 770°C using this system. After the reduction of the anode substrate in moist H2, current 0.5Acm−2 loaded to the cell. And then H2 gas concentration had been reduced by stages. The cell voltage was down to below −6V after H2 gas concentration was reduced to pH2 = 2%. This drastic cell voltage drop and AE signal generation occurred at the same time. It is considered that Ni re-oxidation with fracture started at this time. Local delamination between anode and electrolyte, and also cracks at the electrolyte and cathode were observed after redox test. It was confirmed that AE sensing is effective for redox evaluation.

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.

On-Line Monitoring the Hard Turning Using Distribution Parameters of Acoustic Emission Signal

2015 ◽  
Vol 787 ◽  
pp. 907-911
Author(s):  
J. Bhaskaran
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Hard Turning ◽  
Metal Cutting ◽  
Cubic Boron Nitride ◽  
Emission Signal ◽  
Distribution Parameters ◽  
On Line ◽  
Ae Signal ◽  
Wear Monitoring

In hard turning, tool wear of cutting tool crossing the limit is highly undesirable because it adversely affects the surface finish. Hence continuous, online tool wear monitoring during the process is essential. The analysis of Acoustic Emission (AE) signal generated during conventional machining has been studied by many investigators for understanding the process of metal cutting and tool wear phenomena. In this experimental study on hard turning, the skew and kurtosis parameters of root mean square values of AE signal (AERMS) have been used for online monitoring of a Cubic Boron Nitride (CBN) tool wear.

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