PREDICTION OF REMAINING USEFUL LIFE FOR COMPONENTS IN SSC OF RSG-GAS BASED ON RELIABILITY ANALYSIS

Entin Hartini, Endiah Puji Hastuti, Geni Rina Sunaryo, Aep Saepudin, Sri Sudadiyo, Amir Hamzah, Mike Susmikanti

DOI: http://dx.doi.org/10.17146/tdm.2022.24.1.6400

Abstract


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 the reliability and component availability of the system. For this reason, it is necessary 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 A component from SSC RSG-GAS based on reliability analysis. The method used in this paper is a statistical approach to estimating RUL. The Weibull hazard model is determined for modeling the hazard function so that it can be integrated in the reliability analysis. The model is verified using data from the safety A component from the SSC RSG-GAS. The results obtained from the analysis are useful 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.


Full Text:

PDF

References


  1. 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)
  2. Wienker M., Henderson K., Volkerts J. The Computerized Maintenance Management System an Essential Tool for World Class Maintenance. Procedia Eng. 2016. 138:413–420.
  3. Vishnu C.R., Regikumar V. Reliability Based Maintenance Strategy Selection in Process Plants: A Case Study. Procedia Technol. 2016. 25(Raerest):1080–1087.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.


Refbacks

  • There are currently no refbacks.


PTKRN Digital Library Mendeley