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Lubricants
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Acoustic Emission Techniques in Wear Monitoring
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Acoustic Emission Techniques in Wear Monitoring

A special issue of Lubricants (ISSN 2075-4442).

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 27312

Special Issue Editor

Prof. Dr. Alan Hase

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Faculty of Engineering, Saitama Institute of Technology, 1690 Fusaiji, Fukaya-shi, Saitama 369-0293, Japan
Interests: adhesive wear; abrasive wear; fatigue wear; wear of specific materials; seizure/scoring/scuffing; electrolytic corrosion/electric wear; tribomagnetization; journal bearings; rolling bearings; brakes; machining; ultrasonic/acoustic emission methods; friction and wear testing machines/testing methods; in situ observations; in situ measurements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Acoustic emission (AE) is the emission of elastic stress waves resulting from the deformation and fracture of materials. During tribological processes, AE waves are generated by the deformation and fractures of material surfaces, and considerable information can be obtained by measuring them.

AE techniques have tremendous potential for in situ measurements of tribological characteristics. Furthermore, it is expected to be widely used as a tool to diagnose and evaluate wear phenomena that are very complex and changeable. However, to apply AE techniques to identify and evaluate tribological phenomena and their characteristics, relationships between AE signals and tribological phenomena must be fully understood.

In the Special Issue entitled "Acoustic Emission Techniques in Wear Monitoring", original papers focusing on wear monitoring by AE techniques for various tribo-materials and friction systems are welcomed. We hope that this Special Issue will be utilized to make breakthroughs in the evaluation and in situ measurement in the tribology field. We are looking forward to receiving your submission.

Prof. Dr. Alan Hase
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Lubricants is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Acoustic emission
  • Adhesive wear
  • Abrasive wear
  • Fatigue wear
  • Sliding friction
  • Rolling friction
  • Bearings
  • Coatings
  • Lubricants
  • Condition monitoring

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Related Special Issue

Published Papers (5 papers)

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Research

24 pages, 21739 KiB  
Article
A Time-Frequency Based Approach for Acoustic Emission Assessment of Sliding Wear
by Igor Rastegaev, Dmitry Merson, Inna Rastegaeva and Alexei Vinogradov
Lubricants 2020, 8(5), 52; https://doi.org/10.3390/lubricants8050052 - 9 May 2020
Cited by 15 | Viewed by 3817
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Acoustic Emission Techniques in Wear Monitoring)
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11 pages, 4060 KiB  
Article
Tribolumen: A Tribometer for A Correlation Between AE Signals and Observation of Tribological Process in Real-Time—Application to A Dry Steel/Glass Reciprocating Sliding Contact
by Khouloud Jlaiel, Malik Yahiaoui, Jean-Yves Paris and Jean Denape
Lubricants 2020, 8(4), 47; https://doi.org/10.3390/lubricants8040047 - 14 Apr 2020
Cited by 9 | Viewed by 3115
Abstract
This paper deals with the development of an original apparatus called TRIBOLUMEN designed specifically for friction experiments on transparent materials. The friction measurement is synchronized with an acoustic emission (AE) sensor and the device is also equipped with a high-speed camera offering a [...] Read more.
This paper deals with the development of an original apparatus called TRIBOLUMEN designed specifically for friction experiments on transparent materials. The friction measurement is synchronized with an acoustic emission (AE) sensor and the device is also equipped with a high-speed camera offering a direct view at the interface to gain a deeper understanding of tribological mechanisms. The TRIBOLUMEN device is in ball-on-flat contact configuration with a range of strokes from 5 to 500 µm and an oscillation frequency from 5 to 600 Hz. The experiments showed that this device has an adequate rigidity and can detect subtle friction modifications of the oscillating contacts. The observation of a steel-on-glass contact in real-time highlighted the initiation of Hertzian cracks followed by the formation of debris in the contact. Using the synchronous measurement, these mechanisms were clearly associated with different stages in the friction measurement and in the AE signals, which permitted to identify the AE signature of Hertzian cracks. Full article
(This article belongs to the Special Issue Acoustic Emission Techniques in Wear Monitoring)
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14 pages, 8354 KiB  
Article
Early Detection and Identification of Fatigue Damage in Thrust Ball Bearings by an Acoustic Emission Technique
by Alan Hase
Lubricants 2020, 8(3), 37; https://doi.org/10.3390/lubricants8030037 - 24 Mar 2020
Cited by 35 | Viewed by 7833
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Acoustic Emission Techniques in Wear Monitoring)
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27 pages, 13195 KiB  
Article
Friction and Wear Monitoring Methods for Journal Bearings of Geared Turbofans Based on Acoustic Emission Signals and Machine Learning
by Noushin Mokhtari, Jonathan Gerald Pelham, Sebastian Nowoisky, José-Luis Bote-Garcia and Clemens Gühmann
Lubricants 2020, 8(3), 29; https://doi.org/10.3390/lubricants8030029 - 7 Mar 2020
Cited by 39 | Viewed by 7383
Abstract
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: [...] Read more.
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. Full article
(This article belongs to the Special Issue Acoustic Emission Techniques in Wear Monitoring)
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23 pages, 12913 KiB  
Article
Investigation of Galling Wear Using Acoustic Emission Frequency Characteristics
by Vignesh. V. Shanbhag, Bernard. F. Rolfe and Michael. P. Pereira
Lubricants 2020, 8(3), 25; https://doi.org/10.3390/lubricants8030025 - 2 Mar 2020
Cited by 12 | Viewed by 4014
Abstract
In the sheet metal stamping process, during sliding contact between the tool and sheet, it is expected that severe events such as tool wear or fracture on the sheet generate acoustic emission (AE) burst waveforms. Attempts have been made in the literature to [...] Read more.
In the sheet metal stamping process, during sliding contact between the tool and sheet, it is expected that severe events such as tool wear or fracture on the sheet generate acoustic emission (AE) burst waveforms. Attempts have been made in the literature to correlate the AE burst waveform with the wear mechanisms. However, there is a need for additional studies to understand the frequency characteristics of the AE burst waveform due to the severity and progression of the galling wear. This paper will determine the AE frequency characteristics that can be used to monitor galling wear, independent of the experimental process examined. The AE burst waveforms generated during the stamping and scratch tests are analysed in this paper to understand the change in the AE frequency characteristics with the galling severity. These AE burst waveforms were investigated using the Hilbert Huang Transform (HHT) time-frequency technique, band power, and mean-frequency. Subsequently, these AE frequency features are correlated with the wear behaviour observed via high-resolution profilometer images of the stamped parts and scratch surfaces. Initially, the HHT technique is applied to the AE burst waveform to understand the influence of wear severity in the power distribution over the wide AE frequency range. Later, the AE bandpower feature is used to quantitatively analyse the power in each frequency interval during the unworn and worn tool conditions. Finally, the mean-frequency of AE signal is identified to be able to determine the onset of galling wear. The new knowledge defined in this paper is the AE frequency features and wear measurement feature that can be used to indicate the onset of galling wear, irrespective of the processes examined. Full article
(This article belongs to the Special Issue Acoustic Emission Techniques in Wear Monitoring)
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