Open Access
Mechanics & Industry
Volume 22, 2021
Article Number 29
Number of page(s) 9
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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.