AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Friction Article
PDF (2.6 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions

Florian KÖNIG1( ), Florian WIRSING1, Georg JACOBS1, Rui HE2, Zhigang TIAN2, Ming J. ZUO2,3
Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Aachen 52062, Germany
Department of Mechanical Engineering, University of Alberta, Edmonton T6G1H9, Canada
Qingdao International Academician Park Research Institute, Qingdao 266000, China
Show Author Information

Graphical Abstract

Abstract

This study introduces a method to predict the remaining useful life (RUL) of plain bearings operating under stationary, wear-critical conditions. In this method, the transient wear data of a coupled elastohydrodynamic lubrication (mixed-EHL) and wear simulation approach is used to parametrize a statistical, linear degradation model. The method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient, system-dependent wear progression within the RUL prediction. A case study is used to show the suitability of the proposed method. The results show that the method can be applied to three distinct types of post-wearing-in behavior: wearing-in with subsequent hydrodynamic, stationary wear, and progressive wear operation. While hydrodynamic operation leads to an infinite lifetime, the method is successfully applied to predict RUL in cases with stationary and progressive wear.

Keywords

Bayesian inferencewear modelingplain bearingsremaining useful life prediction

References

[1]
Bobzin K, Wietheger W, Jacobs G, Bosse D, Schröder T, Rolink A. Thermally sprayed coatings for highly stressed sliding bearings. Wear 458–459: 203415 (2020)
Crossref Google Scholar
[2]
Chun S M, Khonsari M M. Wear simulation for the journal bearings operating under aligned shaft and steady load during start-up and coast-down conditions. Tribol Int 97: 440–466 (2016)
Crossref Google Scholar
[3]
Sohn C, Andert J, Jolovic D. An analysis of the tradeoff between fuel consumption and ride comfort for the pulse and glide driving strategy. IEEE Trans Veh Technol 69(7): 7223–7233 (2020)
Crossref Google Scholar
[4]
Cai J L, Xiang G, Li S, Guo J A, Wang J X, Chen S A, Yang T Y. Mathematical modeling for nonlinear dynamic mixed friction behaviors of novel coupled bearing lubricated with low-viscosity fluid. Phys Fluids 34(9): 93612 (2022)
Crossref Google Scholar
[5]
Xiang G, Yang T Y, Guo J, Wang J X, Liu B, Chen S A. Optimization transient wear and contact performances of water-lubricated bearings under fluid-solid-thermal coupling condition using profile modification. Wear 502–503: 204379 (2022)
Crossref Google Scholar
[6]
Wodtke M, Litwin W. Water-lubricated stern tube bearing— Experimental and theoretical investigations of thermal effects. Tribol Int 153: 106608 (2021)
Crossref Google Scholar
[7]
Litwin W. Influence of local bush wear on water lubricated sliding bearing load carrying capacity. Tribol Int 103: 352–358 (2016)
Crossref Google Scholar
[8]
Bouyer J, Fillon M, Pierre-Danos I. Influence of wear on the behavior of a two-lobe hydrodynamic journal bearing subjected to numerous startups and stops. J Tribol 129(1): 205–208 (2007)
Crossref Google Scholar
[9]
Hashimoto H, Wada S, Nojima K. Performance characteristics of worn journal bearings in both laminar and turbulent regimes. Part I: Steady-state characteristics. S L E Trans 29(4): 565–571 (1986)
Crossref Google Scholar
[10]
Hashimoto H, Wada S, Nojima K. Performance characteristics of worn journal bearings in both laminar and turbulent regimes. Part II: Dynamic characteristics. S L E Trans 29(4): 572–577 (1986)
Crossref Google Scholar
[11]
Saridakis K M, Nikolakopoulos P G, Papadopoulos C A, Dentsoras A J. Identification of wear and misalignment on journal bearings using artificial neural networks. Proc Inst Mech Eng Part J 226(1): 46–56 (2012)
Crossref Google Scholar
[12]
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)
Crossref Google Scholar
[13]
Regis A, Linares J M, Arroyave-Tobon S, Mermoz E. Numerical model to predict wear of dynamically loaded plain bearings. Wear 508–509: 204467 (2022)
Crossref Google Scholar
[14]
Vencl A, Rac A. Diesel engine crankshaft journal bearings failures: Case study. Eng Fail Anal 44: 217–228 (2014)
Crossref Google Scholar
[15]
Ligier J L, Noel B. Friction reduction and reliability for engines bearings. Lubricants 3(3): 569–596 (2015)
Crossref Google Scholar
[16]
Cubillo A, Perinpanayagam S, Esperon-Miguez M. A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery. Adv Mech Eng 8(8): 168781401666466 (2016)
Crossref Google Scholar
[17]
Takabi J, Khonsari M M. On the thermally-induced seizure in bearings: A review. Tribol Int 91: 118–130 (2015)
Crossref Google Scholar
[18]
Hadler J. Tribologische beurteilung und optimierung mischreibungsbeanspruchter radialgleitlager. Ph.D. Thesis. Magdeburg (DE): TU Magdeburg, 1994.
[19]
Begelinger A, De Gee A W J. The effect of wear on the performance of statically loaded journal bearings. S L E Trans 19(4): 273–278 (1976)
Crossref Google Scholar
[20]
Rodermund H. Zur auslegung von radialgleitlagern im mischreibungsgebiet. (In German). Tribologie + Schmierungstechnik 33(5): 272–274 (1986)
Google Scholar
[21]
Maier M, Pusterhofer M, Grün F. Wear simulation in lubricated contacts considering wear-dependent surface topography changes. Mater Today Proc, (2023)
Crossref Google Scholar
[22]
König F, Ouald Chaib A, Jacobs G, Sous C. A multiscale-approach for wear prediction in journal bearing systems— From wearing-in towards steady-state wear. Wear 426–427: 1203–1211 (2019)
Crossref Google Scholar
[23]
Ding N, Li H L, Xin Q, Wu B, Jiang D. Multi-source domain generalization for degradation monitoring of journal bearings under unseen conditions. Reliab Eng Syst Saf 230: 108966 (2023)
Crossref Google Scholar
[24]
König F, Marheineke J, Jacobs G, Sous C, Zuo M J, Tian Z G. Data-driven wear monitoring for sliding bearings using acoustic emission signals and long short-term memory neural networks. Wear 476: 203616 (2021)
Crossref Google Scholar
[25]
Ding N, Li H L, Yin Z W, Zhong N, Zhang L. Journal bearing seizure degradation assessment and remaining useful life prediction based on long short-term memory neural network. Measurement 166: 108215 (2020)
Crossref Google Scholar
[26]
Argatov I, Jin X Q. Time-delay neural network modeling of the running-in wear process. Tribol Int 178: 108021 (2023)
Crossref Google Scholar
[27]
Bote-Garcia J L, Gühmann C. Wear volume estimation for a journal bearing dataset. Tm Tech Mess 89(7–8): 534–543 (2022)
Crossref Google Scholar
[28]
Shutin D, Bondarenko M, Polyakov R, Stebakov I, Savin L. Method for on-line remaining useful life and wear prediction for adjustable journal bearings utilizing a combination of physics-based and data-driven models: A numerical investigation. Lubricants 11(1): 33 (2023)
Crossref Google Scholar
[29]
Dang C, Valdebenito M A, Faes M G R, Wei P F, Beer M. Structural reliability analysis: A Bayesian perspective. Struct Saf 99: 102259 (2022)
Crossref Google Scholar
[30]
König F. Prognose des Verschleißverhaltens ölgeschmierter Gleitlager. Aachen (DE): Verlagsgruppe Mainz GmbH, 2020.
[31]
Offner G, Knaus O. A generic friction model for radial slider bearing simulation considering elastic and plastic deformation. Lubricants 3(3): 522–538 (2015)
Crossref Google Scholar
[32]
Zhao F Q, Tian Z G, Liang X H, Xie M J. An integrated prognostics method for failure time prediction of gears subject to the surface wear failure mode. IEEE Trans Reliab 67(1): 316–327 (2018)
Crossref Google Scholar
[33]
Conjugate Bayesian analysis of the Gaussian distribution. https://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf, 2007
[34]
Gebraeel N, Elwany A, Pan J. Residual life predictions in the absence of prior degradation knowledge. IEEE Trans Reliab 58(1): 106–117 (2009)
Crossref Google Scholar
[35]
Bergmann P, Grün F, Summer F, Gódor I. Evaluation of wear phenomena of journal bearings by close to component testing and application of a numerical wear assessment. Lubricants 6(3): 65 (2018)
Crossref Google Scholar
Friction
Volume 12 Issue 6,
June 2024
Pages 1272-1282
DOI: 10.1007/s40544-023-0814-y
Cite this article:
KÖNIG F, WIRSING F, JACOBS G, et al. Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions. Friction, 2024, 12(6): 1272-1282. https://doi.org/10.1007/s40544-023-0814-y

127

Views

15

Downloads

1

Crossref

1

Web of Science

2

Scopus

0

CSCD

Altmetrics
Received: 17 April 2023
Revised: 16 June 2023
Accepted: 07 August 2023
Published: 15 December 2023
© The author(s) 2023.

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/.
Return