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.

The Online Monitoring of Surface Quality Based on Time-Frequency Spectrum Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 564-568
Author(s):  
Chang Liu ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Lu Zhang
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Surface Quality ◽  
Machining Process ◽  
Work Piece ◽  
Time Frequency ◽  
Quality Of Work ◽  
On Line ◽  
Ae Signal

There is a high requirement on the surface quality of work-pieces made of Ni-based super-alloys due to the important application in aviation and aerospace fields, so it is particularly important to implement the on-line monitoring to the surface quality of work-piece in the machining process. The acoustic emission (AE) signal has the relatively superior signal/noise ratio and sensitivity during the process of nickel alloy. Through the analysis of AE signal’s characteristic which comes from the different condition of tool wear, it is an effective mean to evaluate the tool wear condition and monitor the surface quality of work-piece due to the usage of AE during the machining process. This paper indicate that it is simple and intuitive to achieve the on-line monitoring of surface quality which based on spectrum analysis of AE signal and proposed the method of on-line monitoring of the nickel alloy surface quality under different condition of tool wear based on AE time-frequency spectrum.

The Study of Quality Monitoring and Control for Laser Cutting Based on Pulse-Induced Acoustic Sound

2012 ◽  
Vol 182-183 ◽  
pp. 422-426 ◽  
Author(s):  
Hui Juan Hao ◽  
Guang He Cheng ◽  
Ji Yong Xu
Keyword(s):  
Adaptive Control ◽  
Laser Cutting ◽  
Cutting Speed ◽  
Processing Parameters ◽  
Wavelet Denoising ◽  
Quality Monitoring ◽  
Monitoring And Control ◽  
Time Frequency ◽  
And Control ◽  
The Impact

In this paper, the pulse-induced acoustic sound in laser cutting is collected, and the data processing is performed with wavelet denoising and time-frequncy analyzing. The impact of laser processing parameters on the acoustic signal is discussed; and further analysis of the effect of cutting speed is conducted. The corresponding relationship between the best velocity and the maximum time-frequency energy density is got; also the plan of adaptive control in laser cutting is designed. The results in this paper can provide important parameters for adaptive control of laser cutting.

Infrared Sensing for On-Line Weld Geometry Monitoring and Control

1995 ◽  
Vol 117 (3) ◽  
pp. 323-330 ◽  
Author(s):  
P. Banerjee ◽  
S. Govardhan ◽  
H. C. Wikle ◽  
J. Y. Liu ◽  
B. A. Chin
Keyword(s):  
Plate Thickness ◽  
Welding Process ◽  
Temperature Gradients ◽  
Monitoring And Control ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Weld Geometry ◽  
On Line ◽  
And Control ◽  
Welding Processes

This paper describes a method for on-line weld geometry monitoring and control using a single front-side infrared sensor. Variations in plate thickness, shielding gas composition and minor element content are known to cause weld geometry changes. These changes in the weld geometry can be distinctly detected from an analysis of temperature gradients computed from infrared data. Deviations in temperature gradients were used to control the bead width and depth of penetration during the welding process. The analytical techniques described in this paper have been used to control gas tungsten arc and gas metal arc welding processes.

On-line monitoring of WEDM-LS surface roughness by utilizing time-frequency characteristics of AE signal

2017 ◽  
Vol 25 (8) ◽  
pp. 2090-2097
Author(s):  
王 艳 WANG Yan ◽  
王健波 WANG Jian-bo ◽  
王 强 WANG Qiang ◽  
熊 巍 Xiong Wei ◽  
贺独醒 HE Du-xing
Keyword(s):  
Surface Roughness ◽  
Frequency Characteristics ◽  
Time Frequency ◽  
On Line ◽  
Ae Signal

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.

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