Risk Evaluation in FMEA when the Failure Severity Depends on the Detection Time

FMEA에서 고장 심각도의 탐지시간에 따른 위험성 평가

  • Jang, Hyeon Ae (Busan Innovation Center for Quality) ;
  • Yun, Won Young (Department of Industrial Engineering, Pusan National University) ;
  • Kwon, Hyuck Moo (Department of System Management and Engineering, Pukyong National University)
  • 장현애 (사단법인 부산품질혁신센터) ;
  • 윤원영 (부산대학교 산업공학과) ;
  • 권혁무 (부경대학교 시스템경영공학과)
  • Received : 2016.04.25
  • Accepted : 2016.08.09
  • Published : 2016.08.31


The FMEA is a widely used technique to pre-evaluate and avoid risks due to potential failures for developing an improved design. The conventional FMEA does not consider the possible time gap between occurrence and detection of failure cause. When a failure cause is detected and corrected before the failure itself occurs, there will be no other effect except the correction cost. But, if its cause is detected after the failure actually occurs, its effects will become more severe depending on the duration of the uncorrected failure. Taking this situation into account, a risk metric is developed as an alternative to the RPN of the conventional FMEA. The severity of a failure effect is first modeled as linear and quadratic severity functions of undetected failure time duration. Assuming exponential probability distribution for occurrence and detection time of failures and causes, the expected severity is derived for each failure cause. A new risk metric REM is defined as the product of a failure cause occurrence rate and the expected severity of its corresponding failure. A numerical example and some discussions are provided for illustration.


Supported by : Pukyong National University


  1. H. Liu, L. Liu and N. Liu, "Risk evaluation approaches in failure mode and effects analysis: A literature review", Expert Systems with Applications, Vol. 40, pp. 828-838, 2013.
  2. Ford Design Institute, "FMEA HANDBOOK", VERSION 4.1, 2004.
  3. H. M. Kwon, S. H. Hong, M. K. Lee and A. Sutrisno, "Risk Evaluation Based on the Time Dependence Expected Loss Model in FMEA", Journal of Korean Society of Safety, Vol. 26, No. 6, pp. 104-110, 2011.
  4. M. Abdelgawad and A. R. Fayek, "Risk management in the construction industry using combined Fuzzy FMEA and Fuzzy AHP", Journal of Construction Engineering and management, Vol. 136, pp. 1028-1036, 2010.
  5. M. Kumru and P. Y. Kumru, "Fuzzy FMEA application to improve purchasing process in a public hospital", Applied Soft Computing, Vol. 13, No. 1, pp. 721-733, 2013.
  6. H. C. Liu, L. Liu and P. Li, "Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator", International Journal of Systems Science,Vol. 45, No. 10, pp. 2012-2030, 2014.
  7. H. C. Liu, X. J. Fan, P. Li and Y. Z. Chen, "Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment", Engineering Applications of Artificial Intelligence, Vol. 34, pp. 168-177, 2014.
  8. H. C. Liu, J. X. You, X. J. Fan and Q. L. Liu, "Failure mode and effects analysis using D numbers and grey relational projection method", Expert Systems with Applications, Vol. 41, pp. 4670-4679, 2014.
  9. H. M. Kwon, S. H. Hong and M. K. Lee, "An expected loss model for FMEA under periodic monitoring of failure causes", Journal of the Korean Institute of Industrial Engineers, Vol. 39, No. 2, pp. 143-148, 2013.
  10. S. J. Rhee and K. Ishii, "Using cost based FMEA to enhance reliability and serviceability", Advanced Engineering Informatics, Vol. 17, pp. 179-188, 2003.
  11. M. S. Baek, H. A. Jang and H. M. Kwon, "A modified Metric of FMEA for Risk Evaluation Based on ASIL of Safety System", Journal Korean Society Quality Management, Vol. 42, No. 4, pp. 543-562, 2014.
  12. H. Zhang, W. Li and J. Quin, "Model-based functional safety analysis method for automotive embedded system application", International Conference on Intelligent Control and Information Proceedings, China: Dalian, pp. 13-15, 2010.