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Spatial-temporal modeling of oil condition monitoring: A review

Author

Listed:
  • Pan, Yan
  • Liang, Bin
  • Yang, Lei
  • Liu, Houde
  • Wu, Tonghai
  • Wang, Shuo

Abstract

Lubricating oil plays a vital role as the information carrier for equipment tribological performance. Therefore, oil condition monitoring (OCM) serves as a crucial technology for assessing and predicting state degradation, providing the first-line defense against functional failure. However, limited by random fluctuation and insufficient knowledge, the modeling of OCM has suffered from uncertainty problems, leading to poor applicability and insufficient generalizability. The existing reviews are less elaborated on the information uncertainty for description, characterization, and treatment, which expresses the essence of modeling constraints. To bridge this gap, the paper provides a comprehensive review of the oil state modeling in terms of uncertainty. The existing methods and metrics are reviewed from the perspective of the spatial oil state assessment (OCA) and temporal remaining useful life (RUL) prediction modeling. Further, the existing methods are analyzed to solve the uncertainty problem, and then the solutions in oil state modeling considering uncertainty are discussed. Finally, targeting the challenges of the monitoring technology, the future trends of OCM are presented.

Suggested Citation

  • Pan, Yan & Liang, Bin & Yang, Lei & Liu, Houde & Wu, Tonghai & Wang, Shuo, 2024. "Spatial-temporal modeling of oil condition monitoring: A review," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:reensy:v:248:y:2024:i:c:s0951832024002564
    DOI: 10.1016/j.ress.2024.110182
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    References listed on IDEAS

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    1. Vališ, David & Žák, Libor & Pokora, Ondřej & Lánský, Petr, 2016. "Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 231-242.
    2. Chang, Mingu & Lee, Jongsoo, 2020. "Early stage data-based probabilistic wear life prediction and maintenance interval optimization of driving wheels," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    3. Wang, Wenbin & Hussin, B. & Jefferis, Tim, 2012. "A case study of condition based maintenance modelling based upon the oil analysis data of marine diesel engines using stochastic filtering," International Journal of Production Economics, Elsevier, vol. 136(1), pages 84-92.
    4. Talavera, Alejandro & Aguasca, Ricardo & Galván, Blas & Cacereño, Andrés, 2013. "Application of Dempster–Shafer theory for the quantification and propagation of the uncertainty caused by the use of AIS data," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 95-105.
    5. W Wang & B Hussin, 2009. "Plant residual time modelling based on observed variables in oil samples," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 789-796, June.
    6. Yin, Xiuxian & He, Wei & Cao, You & Ma, Ning & Zhou, Guohui & Li, Hongyu, 2024. "A new health state assessment method based on interpretable belief rule base with bimetric balance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Bin Liu & Xiujie Zhao & Yiqi Liu & Phuc Do, 2021. "Maintenance optimisation for systems with multi-dimensional degradation and imperfect inspections," International Journal of Production Research, Taylor & Francis Journals, vol. 59(24), pages 7537-7559, December.
    8. Liu, Bin & Zhao, Xiujie & Liu, Guoquan & Liu, Yiqi, 2020. "Life cycle cost analysis considering multiple dependent degradation processes and environmental influence," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    9. Wakiru, James & Pintelon, Liliane & Muchiri, Peter N. & Chemweno, Peter K. & Mburu, Stanley, 2020. "Towards an innovative lubricant condition monitoring strategy for maintenance of ageing multi-unit systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    10. Jiang, R., 2013. "A multivariate CBM model with a random and time-dependent failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 178-185.
    11. W Wang, 2007. "A prognosis model for wear prediction based on oil-based monitoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(7), pages 887-893, July.
    12. Zheng, Xiao-Wei & Li, Hong-Nan & Gardoni, Paolo, 2023. "Hybrid Bayesian-Copula-based risk assessment for tall buildings subject to wind loads considering various uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    13. Yu, Wennian & Tu, Wenbing & Kim, Il Yong & Mechefske, Chris, 2021. "A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    14. Wenbin Wang & Wenjuan Zhang, 2005. "A model to predict the residual life of aircraft engines based upon oil analysis data," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(3), pages 276-284, April.
    15. Pan, Yan & Jing, Yunteng & Wu, Tonghai & Kong, Xiangxing, 2021. "An Integrated Data and Knowledge Model Addressing Aleatory and Epistemic Uncertainty for Oil Condition Monitoring," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    16. Li, Xinglin & Xie, Luofeng & Deng, Bo & Lu, Houhong & Zhu, Yangyang & Yin, Ming & Yin, Guofu & Gao, Wenxiang, 2024. "Deep dynamic high-order graph convolutional network for wear fault diagnosis of hydrodynamic mechanical seal," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    17. Rashid, H.S.J. & Place, C.S. & Mba, D. & Keong, R.L.C. & Healey, A. & Kleine-Beek, W. & Romano, M., 2015. "Reliability model for helicopter main gearbox lubrication system using influence diagrams," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 50-57.
    18. D Lin & D Banjevic & A K S Jardine, 2006. "Using principal components in a proportional hazards model with applications in condition-based maintenance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(8), pages 910-919, August.
    19. Pan, Yan & Jing, Yunteng & Wu, Tonghai & Kong, Xiangxing, 2022. "Knowledge-based data augmentation of small samples for oil condition prediction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    20. Drieschner, Martin & Petryna, Yuri & Gruhlke, Robert & Eigel, Martin & Hömberg, Dietmar, 2020. "Comparison of various uncertainty models with experimental investigations regarding the failure of plates with holes," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    21. Yoo, Yeongmin & Jung, Ui-Jin & Han, Yong Ha & Lee, Jongsoo, 2021. "Data Augmentation-Based Prediction of System Level Performance under Model and Parameter Uncertainties: Role of Designable Generative Adversarial Networks (DGAN)," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    22. Liu, Xingchen & Sun, Qiuzhuang & Ye, Zhi-Sheng & Yildirim, Murat, 2021. "Optimal multi-type inspection policy for systems with imperfect online monitoring," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    23. Si, Xiao-Sheng & Li, Tianmei & Zhang, Jianxun & Lei, Yaguo, 2022. "Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    24. Li, Xiao-Yang & Chen, Wen-Bin & Kang, Rui, 2021. "Performance margin-based reliability analysis for aircraft lock mechanism considering multi-source uncertainties and wear," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    25. Certa, Antonella & Hopps, Fabrizio & Inghilleri, Roberta & La Fata, Concetta Manuela, 2017. "A Dempster-Shafer Theory-based approach to the Failure Mode, Effects and Criticality Analysis (FMECA) under epistemic uncertainty: application to the propulsion system of a fishing vessel," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 69-79.
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