Skip to main content

Study of RCM-based maintenance planning for complex structures using soft computing technique


To guarantee the efficiency of maintenance strategies for a complex structure, safety and cost limitations must be considered. This research introduces RCM-based (Reliability Centered Maintenance) life cycle optimization for reasonable maintenance. The design variable is the reliability of each part, which consists of a complex structure, while the objective is to minimize the total cost function in order to maintain the system within the desired system reliability. This research constructs the cost function that can reflect the current operating condition and maintenance characteristics of individual parts by generating essential cost factors. To identify the optimal reliability of each component in a system, this paper uses a Neuro-Evolutionary technique. Additionally, this research analyzes the reliability growth of a system by using the AMSAA (Army Material Systems Analysis Activity) model to estimate the failure rate of each part. The MTBF (Mean Time Between Failure) and the failure rate of the whole system, which is responding to the individual parts, are estimated based on the history data by using neural networks. Finally, this paper presents the optimal life cycle of a complex structure by applying the optimal reliability and the estimated MTBF to the RAMS (Reliability, Availability, Maintainability, and Safety) algorithm.

This is a preview of subscription content, access via your institution.


  • Adamantios, M. (2000). Reliability allocation and optimization for complex systems. Proc. Annual Reliability and Maintainability Symp., 216–221.

  • Fahlman, S. E. and Lebiere, C. (1990). The cascade-correlation learning architecture. Advances in Neural Information Processing Systems II, Morgan Kaufmann.

  • Fogel, L. J., Owens, A. J. and Walsh, M. J. (1966). Artificial Intelligence Through Simulated Evolution. New York. Wiley Publishing.

    MATH  Google Scholar 

  • Garcia Marquez, F. P., Schnid, F. and Collado, J. C. (2003). A reliability centered approach to remote condition monitoring. Reliability Engineering and System Safety, 33–40.

  • Goldberg, D. E. (1989). Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley. Reading. Massachusetts.

    Google Scholar 

  • Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor. Michigan. The University of Michigan Press.

    Google Scholar 

  • Lee, H. Y., Park, K. J., Ahn, T. K., Kim, G. D., Yoon, S. K. and Lee, S. I. (2003). A study on the RAMS for maintenance CALS system for urban transit. Korean Society for Railway 6,2, 108–113.

    Google Scholar 

  • Musa, J. D. (1975). A theory of software reliability and its application. IEEE Trans. Software Engineering, SE-1, 312–327.

    Google Scholar 

  • ReliaSoft (2005). Reliability Growth & Repairable Systems Data Analysis Reference, 37–77.

  • Rechenberg, I. (1973). Evolutions Strategie: Optimierung Technischer Systeme Nach Prinzipien der Biologischen Evolution. Frommann-Holzboog. Stuttgart.

    Google Scholar 

  • Ronald, C. S. (1997). Reliability and cost: question for the engineer. Microelectron Reliab. 37,2, 289–295.

    Article  Google Scholar 

  • Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986). Learning Internal Representations by Error Propagation, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, The MIT Press.

  • Smith, A. M. (1993). Reliability-Centered Maintenance. McGraw-Hill. New York.

    Google Scholar 

  • Wang, W. (2004). Reliability importance of components in a complex system. Proc. Annual Reliability and Maintainability Symp., 118–123.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to M. W. Suh.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Son, Y.T., Kim, B.Y., Park, K.J. et al. Study of RCM-based maintenance planning for complex structures using soft computing technique. Int.J Automot. Technol. 10, 635–644 (2009).

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI:

Key Words