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All issues Volume 22 (2021) Mechanics & Industry, 22 (2021) 29 References
Open Access
Issue
Mechanics & Industry
Volume 22, 2021
Article Number 29
Number of page(s) 9
DOI https://doi.org/10.1051/meca/2021028
Published online 14 April 2021
  1. D.E. Richardson, A literature review of the effects of piston and ring friction and lubricating oil viscosity on fuel economy, SAE Trans. 87, 2619–2638 (1978) [Google Scholar]
  2. J. Biberger, H.J. Fußer, Development of a test method for a realistic, single parameter-dependent analysis of piston ring versus cylinder liner contacts with a rotational tribometer, Tribol. Int. 113, 111–124 (2017) [Google Scholar]
  3. E. Tomanik, El.M. Mansori, R. Souza et al., Effect of waviness and roughness on cylinder liner friction, Tribol. Int. 120, 547–555 (2018) [Google Scholar]
  4. M. Yousfi, S. Mezghani, I. Demirci et al., Smoothness and plateauness contributions to the running-in friction and wear of stratified helical slide and plateau honed cylinder liners, Wear 332, 1238–1247 (2015) [Google Scholar]
  5. M. Soderfjall, A. Almqvist, R. Larsson, Component test for simulation of piston ring-Cylinder liner friction at realistic speeds, Tribol. Int. 104, 57–63 (2016) [Google Scholar]
  6. Y.Z. Zhang, A. Kovalev, N. Hayashi et al., Numerical prediction of surface wear and roughness parameters during running-in for line contacts under mixed lubrication, J. Tribol. 140, 061501 (2018) [Google Scholar]
  7. B. Zabala, A. Igartua, X. Fernandez et al., Friction and wear of a piston ring/cylinder liner at the top dead centre: experimental study and modelling, Tribol. Int. 106, 23–33 (2017) [Google Scholar]
  8. Y. Hamid, A. Usman, S.K. Afaq et al., Numeric based low viscosity adiabatic thermo-tribological performance analysis of piston-skirt liner system lubrication at high engine speed, Tribol. Int. 126, 166–176 (2018) [Google Scholar]
  9. C. Liu, Y.J. Lu, Y.F. Zhang et al., Numerical study on the tribological performance of ring/liner system with consideration of oil transport, ASME J. Tribol. 141, 011701 (2019) [Google Scholar]
  10. C. Liu, Y.J. Lu, Y.F. Zhang et al., Investigation on the frictional performance of surface textured ring-deformed liner conjunction in internal combustion engines, Energies 12, 2761 (2019) [Google Scholar]
  11. B. Fan, S. Feng, Y.T. Che et al., An oil monitoring method of wear evaluation for engine hot tests, Int. J. Adv. Manufactur. Technol. 94, 3199–3207 (2018) [Google Scholar]
  12. O. Altıntas, M. Aksoy, E. Unal et al., Artificial neural network approach for locomotive maintenance by monitoring dielectric properties of engine lubricant, Measurement 145, 678–686 (2019) [Google Scholar]
  13. H. Raposo, J.T. Farinha, I. Fonseca et al., Predicting condition based on oil analysis a case study, Tribol. Int. 135, 65–74 (2019) [Google Scholar]
  14. W. Cao, H. Zhang, N. Wang et al., The gearbox wears state monitoring and evaluation based on on-line wear debris features, Wear 426, 1719–1728 (2019) [Google Scholar]
  15. M. Giorgio, M. Guida, G. Pulcini, A wear model for assessing the reliability of cylinder liners in marine diesel engines, IEEE Trans. Reliab. 56, 158–166 (2007) [Google Scholar]
  16. M. Giorgio, M. Guida, G. Pulcini, A state-dependent wear model with an application to marine engine cylinder liners, Technometrics 52, 172–187 (2010) [Google Scholar]
  17. M. Giorgio, M. Guida, G. Pulcini, The transformed gamma process for degradation phenomena in presence of unexplained forms of unit-to-unit variability, Qual. Reliab. Eng. Int. 34, 1–20 (2018) [Google Scholar]
  18. M. Giorgio, G. Pulcini, A new state-dependent degradation process and related model misidentification problems, Eur. J. Oper. Res. 267, 1027–1038 (2018) [Google Scholar]
  19. M. Giorgio, M. Guida, F. Postiglione et al., Bayesian estimation and prediction for the transformed gamma degradation process, Qual. Reliab. Eng. Int. 34, 543–562 (2018) [Google Scholar]
  20. Y.F. Li, H.Z. Huang, H.L. Zhang et al., Fuzzy sets method of reliability prediction and its application to a turbocharger of diesel engines, Adv. Mech. Eng. 2013, 216192 (2013) [Google Scholar]
  21. M.S. Chang, J.H. Shin, Y.I. Kwon et al., Reliability estimation of pneumatic cylinders using performance degradation data, Int. J. Precis. Eng. Manufactur. 14, 2081–2086 (2013) [Google Scholar]
  22. S. Hermann, F. Ruggeri, Modeling wear in cylinder liners, Qual. Reliab. Eng. Int. 33, 839–851 (2017) [Google Scholar]
  23. Z.X. Zhang, C.H. Hu, X. He et al., Lifetime prognostics for deteriorating systems with time-varying random jumps, Reliab. Eng. Syst. Saf. 167, 338–350 (2017) [Google Scholar]
  24. P. Wiederkehr, T. Siebrecht, N. Potthoff, Stochastic modeling of grain wear in geometric physically-based grinding simulations, CIRP Ann. 67, 3253–3258 (2018) [Google Scholar]
  25. X.J. Xu, Z.Z. Zhao, X.B. Xu et al., Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven models, Knowl. Based Syst. 190, 105324 (2020) [Google Scholar]
  26. A. Panda, A.K. Sahoo, I. Panigrahi et al., Prediction models for on-line cutting tool and machined surface condition monitoring during hard turning considering vibration signal, Mech. Ind. 21, 520 (2020) [Google Scholar]
  27. I. Lazakis, Y. Raptodimos, T. Varelas, Predicting ship machinery system condition through analytical reliability tools and artificial neural networks, Ocean Eng. 152, 404–415 (2018) [Google Scholar]
  28. D.D. Kong, Y.J. Chen, N. Li, Hidden semi-Markov model-based method for tool wear estimation in milling process, Int. J. Adv. Manufactur. Technol. 92, 3647–3657 (2017) [Google Scholar]
  29. D.D. Kong, Y.J. Chen, N. Li, Gaussian process regression for tool wear prediction, Mech. Syst. Signal Process. 104, 556–574 (2018) [Google Scholar]
  30. D.D. Kong, Y.J. Chen, N. Li et al., Relevance vector machine for tool wear prediction, Mech. Syst. Signal Process. 127, 573–594 (2019) [Google Scholar]
  31. S. Dutta, S.K. Pal, R. Sen, On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression, Precis. Eng. 43, 34–42 (2016) [Google Scholar]
  32. G.P. Zhang, J. Wang, S.P. Chang, Predicting running-in wear volume with a SVMR-based model under a small amount of training samples, Tribol. Int. 128, 349–355 (2018) [Google Scholar]
  33. I.I. Argatova, Y.S. Chai, An artificial neural network supported regression model for wear rate, Tribol. Int. 138, 211–214 (2019) [Google Scholar]
  34. Y.F. Yang, Y.L. Guo, Z.P. Huang et al., Research on the milling tool wear and life prediction by establishing an integrated predictive model, Measurement 145, 178–189 (2019) [Google Scholar]
  35. G.P. Zhang, X.J. Liu, W.L. Lu, A parameter prediction model of running-in based on surface topography, J. Eng. Tribol. 227, 1047–1055 (2013) [Google Scholar]
  36. J. Kennedy, R. Eberhart, Particle swarm optimization, in 1995 IEEE International Conference on Neural Networks, Perth, 1995, pp. 1942–1948 [Google Scholar]
  37. R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in Sixth International Symposium on Micro Machine and Human Science, Nagoya, 1995, 39–43 [Google Scholar]
  38. X.S. Yang, Introduction to algorithms for data mining and machine learning, Academic Press, 2019, pp. 129–138 [Google Scholar]

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