Prediction of Remaining Useful Life for Components in SSC of RSGGAS Based on Reliability Analysis
DOI:
https://doi.org/10.17146/tdm.2022.24.1.6400Keywords:
Remaining Component Life, SSC RSG-GAS, Safety category A, Reliability AnalysisAbstract
In the maintenance system, efforts are needed to improve the effectiveness of the maintenance system and organization. For effective maintenance planning, it is necessary to have a good understanding of component availability and the reliability of the system. For this reason, it is crucial to determine the remaining component life using Remaining Useful Life (RUL), so that maintenance tasks can be planned effectively. The purpose of this study is to determine the remaining life of the safety category A component from SSC RSG-GAS based on reliability analysis. The method used in this paper is a statistical approach to estimate the RUL. The Weibull hazard model was selected for modeling the hazard function to be integrated into reliability analysis. The model was verified using data from components with safety category A on SSC from RSG-GAS. The results obtained from the analysis are beneficial for estimating the remaining useful lives of these components which can then be used to plan for effective maintenance and help control unplanned outages. The results obtained can be used for maintenance development and preventive repair planning.
References
Deswandri, Subekti M., Sunaryo G.R. Reliability Analysis of RSG-GAS Primary Cooling System to Support Aging Management Program. J. Phys. Conf. Ser. 2018. 962(1)
https://doi.org/10.1088/1742-6596/962/1/012002
Wienker M., Henderson K., Volkerts J. The Computerized Maintenance Management System an Essential Tool for World Class Maintenance. Procedia Eng. 2016. 138:413-420.
https://doi.org/10.1016/j.proeng.2016.02.100
Vishnu C.R., Regikumar V. Reliability Based Maintenance Strategy Selection in Process Plants: A Case Study. Procedia Technol. 2016. 25(Raerest):1080-1087.
https://doi.org/10.1016/j.protcy.2016.08.211
Okoh C., Roy R., Mehnen J., Redding L. Overview of Remaining Useful Life prediction techniques in Through-life Engineering Services. Procedia CIRP. 2014. 16:158-163.
https://doi.org/10.1016/j.procir.2014.02.006
Qin A., Zhang Q., Hu Q., Sun G., He J., Lin S. Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator. Shock Vib. 2017.
https://doi.org/10.1155/2017/6754968
Ghomghaleh A., Khaloukakaie R., Ataei M., Barabadi A., Qarahasanlou A.N., Rahmani O., et al. Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model. PLOS One. 2020. 15(7):1-16.
https://doi.org/10.1371/journal.pone.0236128
Wang F., Liu X., Liu C., Li H., Han Q. Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model. Shock Vib. 2018.
https://doi.org/10.1155/2018/3013684
Gorjian N., Sun Y., Ma L., Yarlagadda P., Mittinty M. Remaining useful life prediction of rotating equipment using covariate-based hazard models-Industry applications. Aust. J. Mech. Eng. 2017. 15(1):36-45.
https://doi.org/10.1080/14484846.2015.1093251
Huynh K.T., Castro I.T., Barros A., Bérenguer C. On the construction of mean residual life for maintenance decision-making. IFAC Proc. Vol. 2012. 45(20 PART 1):654-659.
https://doi.org/10.3182/20120829-3-MX-2028.00144
Zhang Z., Si X., Hu C., Kong X. Degradation modeling-based remaining useful life estimation: A review on approaches for systems with heterogeneity. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 2015. 229(4):343-355.
https://doi.org/10.1177/1748006X15579322
Zhang B., Xu L., Chen Y., Li A. Remaining Useful Life Based Maintenance Policy for Deteriorating Systems Subject to Continuous Degradation and Shock. Procedia CIRP. 2018. 72:1311-1315.
https://doi.org/10.1016/j.procir.2018.03.207
Wang Y., Shahidehpour M., Guo C. Applications of survival functions to continuous semi-Markov processes for measuring reliability of power transformers. J. Mod. Power Syst. Clean Energy. 2017. 5(6):959-969.
https://doi.org/10.1007/s40565-017-0322-z
Sikorska J.Z., Hodkiewicz M., Ma L. Prognostic modelling options for remaining useful life estimation by industry. Mech. Syst. Signal Process. 2011. 25(5):1803-1836.
https://doi.org/10.1016/j.ymssp.2010.11.018
Ellefsen A.L., Bjorlykhaug E., Esoy V., Ushakov S., Zhang H. Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture. Reliability Engineering and System Safety. 2019. 183:240-251.
