Abstract
It is increasingly important to monitor sliding interfaces within machines, since this is where both energy is lost, and failures occur. Acoustic emission (AE) techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials. However, acoustic data from sliding contacts is notoriously complex and difficult to interpret. Herein, we simultaneously measure coefficient of friction (with a conventional force transducer) and acoustic emission (with a piezoelectric sensor and high acquisition rate digitizer) produced by a steel–steel rubbing contact. Acquired data is then used to train machine learning (ML) algorithms (e.g., Gaussian process regression (GPR) and support vector machine (SVM)) to correlated acoustic emission with friction. ML training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this (e.g., down-sampling, averaging, fast Fourier transforms (FFTs), histograms). Next, fresh, unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured friction. There is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data, with root mean square (RMS) errors as low as 0.03 and Pearson correlation coefficients reaching 0.8. Moreover, predictions remain accurate despite changes in test conditions such as normal load, reciprocating frequency, and stroke length. This paves the way for remote, acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Holmberg K, Erdemir A. Influence of tribology on global energy consumption, costs and emissions. Friction 5(3): 263–284 (2017)
Holmberg K, Kivikytö-Reponen P, Härkisaari P, Valtonen K, Erdemir A. Global energy consumption due to friction and wear in the mining industry. Tribol Int 115: 116–139 (2017)
Anonymous, Machine Condition Monitoring Market by Monitoring Technique (Vibration Monitoring, Thermography, Oil Analysis, Corrosion Monitoring, Ultrasound Emission), Monitoring Process (Online, Portable), Deployment, Offering - Global Forecast to 2027. In MarketsandMarkets, 2022.
Anonymous, Oil Condition Monitoring Market by Product Type (Turbines, Compressors, Engines, Gear Systems, Hydraulic Systems), Sampling Type, Vertical (Transportation, Industrial, Oil & Gas), and Region (2021–2026). In MarketsandMarkets, 2021.
Mazal P, Dvoracek J, Pazdera L. Application of acoustic emission method in contact damage identification. Int J Mater Prod Technol 41(1/2/3/4): 140 (2011)
Feng P P, Borghesani P, Smith W A, Randall R B, Peng Z X. A review on the relationships between acoustic emission, friction and wear in mechanical systems. Appl Mech Rev 72(2): 020801 (2020)
Zhang X, Wang K W, Wang Y, Shen Y, Hu H S. Rail crack detection using acoustic emission technique by joint optimization noise clustering and time window feature detection. Appl Acoust 160: 107141 (2020)
Bol’shakov A M, Andreev Y M. Acoustic-emission testing of vertical steel tanks in hard-to-reach areas of the far north. Russ J Nondestruct Test 55(3): 181–184 (2019)
Crivelli D, McCrory J, Miccoli S, Pullin R, Clarke A. Gear tooth root fatigue test monitoring with continuous acoustic emission: Advanced signal processing techniques for detection of incipient failure. Struct Health Monit 17(3): 423–433 (2018)
Liu Z, Peng Q, He C, Wu B. Time difference mapping method for acoustic emission source location of composite plates. ACTA ACUSTICA 45(3): 385–393 (2020) (in Chinese).
Chernov D V, Matyunin V M, Barat V A, Marchenkov A Y, Elizarov S V. Investigation of acoustic emission in low-carbon steels during development of fatigue cracks. Russ J Nondestruct Test 54(9): 638–647 (2018)
Krampikowska A, Pala R, Dzioba I, Świt G. The use of the acoustic emission method to identify crack growth in 40CrMo steel. Materials 12(13): 2140 (2019)
Nivesrangsan P, Steel J A, Reuben R L. Source location of acoustic emission in diesel engines. Mech Syst Signal Process 21(2): 1103–1114 (2007)
Sun J, Wood R J K, Wang L, Care I, Powrie H E G. Wear monitoring of bearing steel using electrostatic and acoustic emission techniques. Wear 259(7–12): 1482–1489 (2005)
Miettinen J, Siekkinen V. Acoustic emission in monitoring sliding contact behaviour. Wear 181–183: 897–900 (1995)
Lingard S, Ng K K. An investigation of acoustic emission in sliding friction and wear of metals. Wear 130(2): 367–379 (1989)
Boness R J, McBride S L, Sobczyk M. Wear studies using acoustic emission techniques. Tribol Int 23(5): 291–295 (1990)
Mussa A, Krakhmalev P, Bergström J. Sliding wear and fatigue cracking damage mechanisms in reciprocal and unidirectional sliding of high-strength steels in dry contact. Wear 444–445: 203119 (2020)
Chevallier E. Mechanical model of the electrical response from a ring–wire sliding contact. Tribol Trans 63(2): 215–221 (2020)
Yang H J, Hu Y, Chen G X, Zhang W H, Wu G N. Correlation between the wear and vibration of the contact strip in a contact wire rubbing against a contact strip with electrical current. Tribol Trans 57(1): 86–93 (2014)
Jiaa C L, Dornfeld D A. Experimental studies of sliding friction and wear via acoustic emission signal analysis. Wear 139(2): 403–424 (1990)
Fan Y B, Gu F S, Ball A. Modelling acoustic emissions generated by sliding friction. Wear 268(5–6): 811–815 (2010)
Hu S T, Huang W F, Shi X, Peng Z K, Liu X F, Wang Y M. Bi-Gaussian stratified effect of rough surfaces on acoustic emission under a dry sliding friction. Tribol Int 119: 308–315 (2018)
Towsyfyan H, Gu F S, Ball A D, Liang B. Modelling acoustic emissions generated by tribological behaviour of mechanical seals for condition monitoring and fault detection. Tribol Int 125: 46–58 (2018)
Fuentes R, Dwyer-Joyce R S, Marshall M B, Wheals J, Cross E J. Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling. Renew Energy 147: 776–797 (2020)
Suzuki H, Kinjo T, Hayashi Y, Takemoto M, Ono K, Hayashi Y. Wavelet transform of acoustic emission signals. J Acoust Emiss 14(2): 69–84 (1996)
Geng Z, Puhan D, Reddyhoff T. Using acoustic emission to characterize friction and wear in dry sliding steel contacts. Tribol Int 134: 394–407 (2019)
Strablegg C, Renhart P, Summer F, Grün F. Methodology, validation & signal processing of acoustic emissions for selected lubricated tribological contacts. Mater Today Proc 62: 2604–2610 (2022)
Baccar D, Söffker D. Wear detection by means of wavelet-based acoustic emission analysis. Mech Syst Signal Process 60–61: 198–207 (2015)
Hase A, Mishina H, Wada M. Correlation between features of acoustic emission signals and mechanical wear mechanisms. Wear 292–293: 144–150 (2012)
Fuentes R, Howard T P, Marshall M B, Cross E J, Dwyer-Joyce R S. Observations on acoustic emissions from a line contact compressed into the plastic region. Proc Inst Mech Eng Part J J Eng Tribol 230(11): 1371–1376 (2016)
König F, Sous C, Ouald Chaib A, Jacobs G. Machine learning based anomaly detection and classification of acoustic emission events for wear monitoring in sliding bearing systems. Tribol Int 155: 106811 (2021)
Sattari Baboukani B, Ye Z J, G Reyes K, Nalam P C. Prediction of nanoscale friction for two-dimensional materials using a machine learning approach. Tribol Lett 68(2): 57 (2020)
Hasan M S, Kordijazi A, Rohatgi P K, Nosonovsky M. Triboinformatic modeling of dry friction and wear of aluminum base alloys using machine learning algorithms. Tribol Int 161: 107065 (2021)
Strablegg C, Summer F, Renhart P, Grün F. Prediction of friction power via machine learning of acoustic emissions from a ring-on-disc rotary tribometer. Lubricants 11(2): 37 (2023)
Rastegaev I A, Merson D L, Danyuk A V, Afanasyev M A, Vinogradov A. Using acoustic emission signal categorization for reconstruction of wear development timeline in tribosystems: Case studies and application examples. Wear 410–411: 83–92 (2018)
Benabdallah H S, Aguilar D A. Acoustic emission and its relationship with friction and wear for sliding contact. Tribol Trans 51(6): 738–747 (2008)
Hanchi J, Klamecki B E. Acoustic emission monitoring of the wear process. Wear 145(1): 1–27 (1991)
Braga-Neto U. Fundamentals of Pattern Recognition and Machine Learning. Cham: Springer International Publishing, (2020).
Stalph P. Analysis and Design of Machine Learning Techniques: Evolutionary Solutions for Regression, Prediction, and Control Problems. Wiesbaden: Springer Fachmedien Wiesbaden, (2014).
Rasmussen C, Nickisch H. Gaussian Processes for Machine Learning. MIT Press, 2005.
Drezet P, Harrison RF. Directly optimised support vector machines for classification and regression. ACSE Research Report 715, The University of Sheffield (1998)
Friel T, Harrison R. Linear programming support vector machines for pattern classification and regression estimation and the SR Algorithm: Improving speed and tightness of VC bounds in SV algorithms. In ACSE Research Report 706, Sheffield, UK, 1998
Weiss J, Richeton T, Louchet F, Chmelik F, Dobron P, Entemeyer D, Lebyodkin M, Lebedkina T, Fressengeas C, McDonald R J. Evidence for universal intermittent crystal plasticity from acoustic emission and high-resolution extensometry experiments. Phys Rev B 76(22): 224110 (2007)
Miguel M C, Vespignani A, Zapperi S, Weiss J, Grasso J R. Intermittent dislocation flow in viscoplastic deformation. Nature 410(6829): 667–671 (2001)
Richeton T, Dobron P, Chmelik F, Weiss J, Louchet F. On the critical character of plasticity in metallic single crystals. Mater Sci Eng A 424(1–2): 190–195 (2006)
Bougherira Y, Entemeyer D, Fressengeas C, Kobelev N P, Lebedkina T A, Lebyodkin M A. The intermittency of plasticity in an Al3%Mg alloy. J Phys: Conf Ser 240: 012009 (2010)
Lebyodkin M A, Kobelev N P, Bougherira Y, Entemeyer D, Fressengeas C, Gornakov V S, Lebedkina T A, Shashkov I V. On the similarity of plastic flow processes during smooth and jerky flow: Statistical analysis. Acta Mater 60(9): 3729–3740 (2012)
Lebyodkin M A, Shashkov I V, Lebedkina T A, Mathis K, Dobron P, Chmelik F. Role of superposition of dislocation avalanches in the statistics of acoustic emission during plastic deformation. Phys Rev E 88(4): 042402 (2013)
Merchant M E. The Friction and Lubrication of Solids. Bowden F P and Tabor D. New York: Oxford Univ. Press, 1950. 337 pp. $7.00. Science 113(2938): 443–444 (1951)
Acknowledgements
This study was supported by a UK Engineering and Physical Sciences Research Council Ph.D. studentship.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The authors declare no competing interests.
Additional information
Robert GUTIERREZ. He is currently a Ph.D. student in the Department of Mechanical Engineering, Imperial College London. He completed his master’s degree in 2020 also at the Department of Mechanical Engineering, Imperial College London. His research is focused on developing acoustic emission techniques for monitoring rubbing contacts.
Tianshi FANG. He is a Project Leader and Researcher at Shell Lubricants Technology since 2019. He has been working on multiple projects related to computational simulation, theoretical analysis, mathematical modeling, and experimental test of lubricants and coolants. He is a mechanical engineer by education. He obtained his Bachelor’s degree with First Class Honours from the University of Hong Kong in 2012, and his Master’s and Ph.D. degrees from Massachusetts Institute of Technology in 2014 and 2019. His research in graduate school was focused on the computational simulations and modeling of lubricating oil flow in piston-ring systems in automotive engines.
Robert MAINWARING. He studied mechanical engineering at Loughborough University in the UK. After graduation, he was employed by Mirrlees Blackstone Diesels Ltd. as a development engineer responsible for enhancing the efficiency of their 400 mm bore K Major diesel engine. A three year spell with the UK’s National Nuclear Corporation focused on computational fluid dynamics followed. He was recruited by Shell in 1988 to research links between lubricants, engines and particulate emissions. Later roles have included lubricant additive research and Industry committee liaison, underpinning his current roles of Technology Manager for Innovation and Senior Principal Scientist within Shell’s Lubricants Technology group, where he leads a wide range of projects targeted at enhancing, demonstrating and communicating the role of lubricants in the performance of engineering equipment.
Tom REDDYHOFF. He is a reader (∼associate professor) in the Department of Mechanical Engineering, Imperial College London. His research focuses on improving the performance of sliding contacts and often involves developing novel in situ measurement techniques used in combination with numerical modelling.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Gutierrez, R., Fang, T., Mainwaring, R. et al. Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data. Friction 12, 1299–1321 (2024). https://doi.org/10.1007/s40544-023-0834-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40544-023-0834-7