Skip to main content
Log in

Optimal replacement model for the physical component of safety critical smart-world CPSs

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Nowadays CPSs have drawn an upsurge of interests for their enormous potential towards the next generation smart systems where safety is a critical issue. In CPS, large number of IoT devices (sensors, actuators, etc.) are deployed to collect data to support safety critical smart-world CPS infrastructures such as smart city, smart health, smart manufacturing, and smart transportation. The degradation of physical components of safety critical smart-world CPSs would deteriorate the performance of the smart system and lead to loss of human life with significant damage to properties. Hence, planned preventive maintenance replacement of physical components of the system is vital to extend the lifetime of the system, to reduce maintenance cost and to avoid risks that may cause major harm to life and property. In this study, we focus on cost effective preventive replacement strategy that recovers failure of physical components of safety critical smart-world CPSs by mainly considering the deterioration state of the physical component on the availability of the system. In view of the degraded physical component of CPSs, we demonstrate the effects of involving maintenance actions during the deterioration state of physical components of CPSs on its availability. Since timely preventive replacement is crucial to support continuous and effective system operation, we compute the optimum time of the constant-interval of preventive replacement strategy for the physical components of CPSs. In this study, we use Weibull distribution as it is a generalized failure model that has been widely used for process equipment life data analyses. At last, the effects of the shape and the scale parameters on the optimal preventive replacement interval of the physical component are also demonstrated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abernethy R (1993) The new Weibull handbook, 2nd edn. North Palm Beach

  • Al-Alia AR, Gupta R, Al-Nabulsi A (2018) Cyber physical systems role in manufacturing technologies. AIP Conf Proc 1957:050007. https://doi.org/10.1063/1.5034337

    Article  Google Scholar 

  • Alemayehu TS, Kim JH (2017) Dependability analysis of cyber-physical systems. IET Comput Digital Tech 11:231–236

    Article  Google Scholar 

  • Alemayehu TS, Kim JH, Cho WD (2020) Optimum preventive replacement interval for the physical component of cyber physical systems. In: International Workshop on Human-centric Computing and Information Sciences (HCIS2020)

  • Arthur J, Hallinan J (1993) A review of the Weibull distribution. J Qual Technol 25:85–93

    Article  Google Scholar 

  • Badía FG, Berrade MD, Cha JH, Lee H (2018) Optimal replacement policy under a general failure and repair model: minimal versus worse than old repair. Reliab Eng Syst Saf 180:362–372

    Article  Google Scholar 

  • Barlow RE, Hunter LC (1960) Optimal preventive maintenance policies. Oper Res 8:90–100

    Article  Google Scholar 

  • Byun Y, Oh S, Choi M (2020) ICT Agriculture support system for chili pepper harvesting. J Inform Process Syst 16:629–638

    Google Scholar 

  • Chen Z, Xiao H, Zhang R, Shi X, Zhou Y, Lv J, Xu S (2018) Optimization of dynamic preventive maintenance for multicomponent systems. In: IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Canada 591–598

  • Chien YH, Chen JA (2010) Optimal spare ordering policy for preventive replacement under cost effective-ness criterion. Appl Math Model 34:716–724

    Article  MathSciNet  Google Scholar 

  • Dong W, Liu S, Bae SJ (2019) Reliability variation and optimal age replacement schedule of compensated discrete multi-state systems. In: IEEE 6th International Conference on Industrial Engineering and Applications 333–337

  • Dong W, Liu S, Bae SJ, Liu Y (2020) A multi-stage imperfect maintenance strategy for multi-state systems with variable user demands. Comput Ind Eng. https://doi.org/10.1016/j.cie.2020.106508Accessed29June2020

    Article  Google Scholar 

  • Endrenyi J, Aboresheid R, Allan RN et al (2001) The present status of maintenance strategies and the impact of maintenance on reliability. IEEE Trans Power Syst 16:638–646

    Article  Google Scholar 

  • Fritzsche R, Lasch R (2012) An integrated logistics model of spare parts maintenance planning within the aviation industry. Int J Econ Manag Eng 6:1–11

    Google Scholar 

  • Jo JH, Sharma PK, Sicato JCS, Park JH (2019) Emerging technologies for sustainable smart city network security: issues, challenges, and countermeasures. J Inform Process Syst 15:765–784

    Google Scholar 

  • Jonge BD, Scarf PA (2020) A review on maintenance optimization. Eur J Oper Res 285:805–824

    Article  MathSciNet  Google Scholar 

  • Jung H, Kim Y (2018) Basic research for smart community center planning by applying the concept of CPS (Cyber-Physical System) of smart city. Korean Soc Basic Ar, Basic Form Stud Basic Form Stud 19:357–370

    Google Scholar 

  • Kadi DA, Beaucaire C, Cléroux R (1990) A periodic maintenance model with used equipment and random minimal repair. Naval Res Log 37:855–865

    Article  Google Scholar 

  • Lawless J F (2011) Statistical models and methods for lifetime data, 2nd edn. John Wiley & Sons 362

  • Lee EA (2008) Cyber physical systems: design challenges. In: 11th IEEE Int. Symp. Object Oriented Real-Time Distributed Computing (ISORC) 363–369

  • Leu SS, Ying TM (2020) Replacement and maintenance decision analysis for hydraulic machinery facilities at reservoirs under imperfect maintenance. Energies 13(10):2507. https://doi.org/10.3390/en13102507

    Article  Google Scholar 

  • Liu Q, Dong M, Lv W, Ye C (2019) Manufacturing system maintenance based on dynamic programming model with prognostics information. J Intell Manuf 30:1155–1173

    Article  Google Scholar 

  • Manzini R, Regattieri A, Pham H, Ferrari E (2010) Maintenance for industrial systems. Springer, London

  • Matos G, White EL (1998) Application of dynamic reconfiguration in the design of fault-tolerant production cell. In: 4th Int. Conf. Configurable Distribution Systems. 2–9

  • Meng Y, Yi S, Kim H (2019) Health and wellness monitoring using intelligent sensing technique. J Inform Process Syst 15:478–491

    Google Scholar 

  • Murthy Prabhakar DN, Xie M, Jiang R (2004) Weibull models. Wiley-Interscience

  • Park GP, Heo JH, Lee SS, Yoon YT (2011) Generalized reliability centered maintenance modeling through modified semi-Markov chain in power system. J Elect Eng Technol 6:25–31

    Article  Google Scholar 

  • Park DM, Kim SK, Seo YS (2019) S-mote: SMART home framework for common household appliances in IoT network. J Inform Process Syst 15:449–456

    Google Scholar 

  • Pradhan DK (1996) Fault-tolerant computer system design. Prentice Hall, Inc., Upper Saddle River, pp 5–14

    Google Scholar 

  • Ramachandran KP, Said Al Hinai AF (2011) Decision mapping and optimal inspection models for plant maintenance: some case studies. In: Proceedings of the 4th International Multi-Conference on Engineering and Technological Innovation

  • Rebaiaia ML, Ait-Kadi D, Jamshidi A (2017) Periodic replacement strategy: optimality conditions and numerical performance comparisons. Int J Prod Res 55:7135–7152

    Article  Google Scholar 

  • Safaei F, Ahmadi J, Balakrishnan N (2019) A repair and replacement policy for repairable systems based on probability and mean of profits. Reliab Eng Syst Saf 183:143–152

    Article  Google Scholar 

  • Schreibera M, Vernickela K, Richter C, Reinhart G (2019) Integrated production and maintenance planning in cyber-physical production systems. Procedia CIRP 79:534–539

    Article  Google Scholar 

  • Sheu SH, Liu TH, Zhang ZG, Tsai HN (2018) The generalized age maintenance policies with random working times. Reliab Eng Syst Saf 169:503–514

    Article  Google Scholar 

  • Sim HS (2019) A Study on the development and effect of smart manufacturing system in PCB Line. J Inform Process Syst 15:181–188

    Google Scholar 

  • Stenström C, Norrbin P, Parida A, Kumar U (2016) Preventive and corrective maintenance–cost comparison and cost–benefit analysis. Struct Infrastruct Eng 12:603–617

    Article  Google Scholar 

  • Sugier J (2011) Adjusting Markov models to changes in maintenance policy for reliability analysis. Comput Modell New Technol 15:7–20

    Google Scholar 

  • Wäfler J, Heegaard PE (2013) A combined structural and dynamic modelling approach for dependability analysis in smart grid. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing 660–665

  • Wenjie D, Sifeng L, Liangyan T, Yingsai C, Zhigeng F (2019) Reliability variation of multi-state components with inertial effect of deteriorating output performances. Reliab Eng Syst Saf 186:176–185

    Article  Google Scholar 

  • Yang L, Ye ZS, Lee CG, Yang SF, Peng R (2019) A two-phase preventive maintenance policy considering imperfect repair and postponed replacement. Eur J Oper Res 274:966–977

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. NRF-2018R1D1A1B07040573), and (No. NRF-2019R1F1A1063128).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jai-Hoon Kim.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alemayehu, T.S., Kim, JH. & Cho, WD. Optimal replacement model for the physical component of safety critical smart-world CPSs. J Ambient Intell Human Comput 13, 4579–4590 (2022). https://doi.org/10.1007/s12652-021-03137-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03137-5

Keywords

Navigation