효율적 유지보수를 위한 도시철도 전동차 브레이크의 시스템 신뢰도 최적화

Reliability Optimization of Urban Transit Brake System For Efficient Maintenance

  • 배철호 (성균관대학교 대학원 기계공학부) ;
  • 김현준 (성균관대학교 대학원 기계공학부) ;
  • 이정환 (성균관대학교 대학원 기계공학부) ;
  • 김세훈 (성균관대학교 대학원 기계공학부) ;
  • 이호용 (한국철도기술연구원) ;
  • 서명원 (성균관대학교 기계공학부)
  • 발행 : 2007.01.01


The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.


  1. Laura Painton and James Campbell, 1995, ' Genetic Algorithms in Optimization of System Reliability,' IEEE TRANSACTIONS ON RELIABILITY, Vol. 44, No. 2, pp. 172-178
  2. Adamantios Mettas, 2000, 'Reliability Allocation and Optimization For Complex Systems,' PROCEEDINGS Annual RELIABILITY and MAINTAINABILITY Symposium, pp. 216-221
  3. Robert H. Sues and Mark A. Cesare, 2005, 'System Reliability and Sensitivity Factors via the MPPSS Method,' Probabilistic Engineering Mechanics, Vol. 20, No. 2, pp. 148-157
  4. Kim, K. H., Bae, C. H., Lee, H. Y. and Suh, M. W., 2004, 'A Study on the Standardization of Failure Classification Method for IT Maintenance System of Urban Transit,' The Korea Society of Automotive Engineers Fall Conference, Vol. 3, pp. 1361-1365
  5. Lee, H. Y., Bae, C. H., Kim, K. H. and Suh, M. W., 2005, 'A Study on Implementation of a BOM Management System Using Component Technique for Maintenance System of Urban Transit,' The Korean Society of Mechanical Enginerrs A, Vol.29, No.1, pp.124-131
  6. Furukawa, T. and Yagawa, G., 1995, 'Parameter Indentification Using an Evolutionary Algorithm and Its Performance Under Measurement Errors,' Computational Mechanics '95, Springer, pp. 122-127
  7. Rumelhart, D. E., Hinton, G. E. and Williams, R. J., 1986, 'Learning Internal Representations by Error Propagation,' In Parallel distributed processing: exploration in the micro-structure of cognition, Vol. 1, pp. 318-362
  8. Holland, J. H., 1975, Adaptation in Natural and Artificial Systems, Ann Arbor, Michigan: The University of Michigan Press
  9. Mitsuo Gen, Runwei Cheng, 1997, Genetic Algorithms and Engineering Design., A Wiley- Interscience Publication
  10. 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, Vol. 6 No. 2, pp. 108-113
  11. Bae, C. H., Kim, S. B., Lee, H. Y., Chang, S. H. and Suh, M. W., 2005, 'A Study on Development of the Reliability Evaluation System for VVVF Urban Transit,' Transactions of KSAE, Vol. 13, No. 5, pp. 7-18