Feasibility Study on the Fault Tree Analysis Approach for the Management of the Faults in Running PCR Analysis

PCR 과정의 오류 관리를 위한 Fault Tree Analysis 적용에 관한 시범적 연구

  • Lim, Ji-Su (Department of Food Science and Technology, Dongguk University) ;
  • Park, Ae-Ri (Department of Food Science and Technology, Dongguk University) ;
  • Lee, Seung-Ju (Department of Food Science and Technology, Dongguk University) ;
  • Hong, Kwang-Won (Department of Food Science and Technology, Dongguk University)
  • Published : 2007.12.31


FTA (fault tree analysis), an analytical method for system failure management, was employed in the management of faults in running PCR analysis. PCR is executed through several processes, in which the process of PCR machine operation was selected for the analysis by FTA. The reason for choosing the simplest process in the PCR analysis was to adopt it as a first trial to test a feasibility of the FTA approach. First, fault events-top event, intermediate event, basic events-were identified by survey on expert knowledge of PCR. Then those events were correlated deductively to build a fault tree in hierarchical structure. The fault tree was evaluated qualitatively and quantitatively, yielding minimal cut sets, structural importance, common cause vulnerability, simulation of probability of occurrence of top event, cut set importance, item importance and sensitivity. The top event was 'errors in the step of PCR machine operation in running PCR analysis'. The major intermediate events were 'failures in instrument' and 'errors in actions in experiment'. The basic events were four events, one event and one event based on human errors, instrument failure and energy source failure, respectively. Those events were combined with Boolean logic gates-AND or OR, constructing a fault tree. In the qualitative evaluation of the tree, the basic events-'errors in preparing the reaction mixture', 'errors in setting temperature and time of PCR machine', 'failure of electrical power during running PCR machine', 'errors in selecting adequate PCR machine'-proved the most critical in the occurrence of the fault of the top event. In the quantitative evaluation, the list of the critical events were not the same as that from the qualitative evaluation. It was because the probability value of PCR machine failure, not on the list above though, increased with used time, and the probability of the events of electricity failure and defective of PCR machine were given zero due to rare likelihood of the events in general. It was concluded that this feasibility study is worth being a means to introduce the novel technique, FTA, to the management of faults in running PCR analysis.

FTA(fault tree analysis)는 system 오류 관리를 위한 정성적/정량적 기법으로 적용되고 있다. FTA를 적용한 PCR의 오류 관리 system의 구축을 위한 시범적 단계로서 PCR 실행의 여러 단계 중 가장 간단한 단계인 '반응액의 제조 및 PCR 기기 사용 단계'를 모델로 하여 분석하였다. PCR 실행시 발생할 수 있는 오류를 연역적 논리 방식에 의해 fault tree의 형태로 규명하였다. Fault tree는 오류 관리의 최상위 요소인 top event를 중심으로 중간 계층을 이루는 intermediate events와 최하위의 요소인 basic events로 세분하여 구성하였다. Top event는 '반응액의 제조 및 PCR 기기 사용 단계에서의 오류'; 중간계층 events는 '기기 유래 오류', '실험행위 유래 오류'; basic events는 '정전상황', 'PCR 기기 선정', '기기 사용 관리', '기기 내구성', '조작의 오류', '시료 구분의 오류'로 분석되었다. 이로부터 top event의 원인 분석 및 중요 관리점을 도출하기 위하여 정성적/정량적 분석을 실시하였다. 정성적 기법으로 minimal cut sets, structural importance, common cause vulnerability를 분석하였고, 정량적 기법으로 simulation, cut set importance, item importance, sensitivity를 분석하였다. 정성적 분석과 정량적 분석의 결과에서 '시료 구분의 오류'와 '기기 조작의 오류'가 제 1중요관리점; '기기 관리의 오류'와 '내구성에 의한 오류'는 제 2중요관리점으로 일치되게 나타났다. 그러나 '정전상황'과 '기기 선정의 오류'는 정성적 분석에서만 중요관리점으로 분석되었다. 특히 sensitivity 분석에서 '기기 관리의 오류'는 사용 시간이 경과함에 따라 가장 중요한 관리점으로 부각되었다. 결론적으로 FTA는 PCR 모델 case에 대한 오류의 원인 분석 및 그 방지를 위한 중요관리점을 제시함에 따라, 궁극적으로 미래에 PCR의 오류 관리 system을 완성할 수 있는 효과적인 방법으로 사료된다.


  1. Hong, K. W. (2006) Rapid detection method for foodborne pathogens-PCR. Safe Food. 1, 16-23
  2. Schneegass, I. and Kohler, J. M. (2001) Flow-through polymerase chain reactions in chip thermocyclers. Molecular Biotech. 82, 101-121 https://doi.org/10.1016/S1389-0352(01)00033-2
  3. White, B. A. (1993) In PCR protocols: Current methods and applications. Humana press inc., New Jersey
  4. Lee, K. H., and Lee, D. H. (1990) An approximate approach for computing fault-tree probabilities. J. KSQC. 18, 25-32
  5. Lee, W. S., Grosh, D. L., Tillman F. A. and Lie, C. H. (1985) Fault tree analysis, methods, and applications-a review. IEEE Trans. Reliability. 34, 194-203 https://doi.org/10.1109/TR.1985.5222114
  6. James, J. J., Reynaldo, F. Jr. and Reva, R. (2001) Fault tree analysis for exposure to refrigerants used for automotive air conditioning in the united states. Risk Analysis. 21, 157-170 https://doi.org/10.1111/0272-4332.211098
  7. Bertolinia, M., Rizzia, A. and Bevilacqua, M. (2007) An alternative approach to HACCP system implementation. J. Food Engineering. 79, 1322-1328 https://doi.org/10.1016/j.jfoodeng.2006.04.038
  8. Kaiser, B., Gramlich, C. and Foster, M. (2007) State/event fault trees-A safety analysis model for software-controlled system. Reliability Engineering & System Safety. 92, 1521-1537 https://doi.org/10.1016/j.ress.2006.10.010
  9. Jung, W. D. and Hwang, M., J. (1997) Safety and reliability assessment for nuclear power plants. J. Korea Safety. 12, 143-152
  10. Vesely, W. E. and Goldberg, F. F. (1981) In Fault tree handbook. U.S. Nuclear regulatory commission
  11. Ortmeier, F. and Schellhorn, G. (2007) Formal fault tree analysis-practical experiences. Electronic Notes in Theoretical Computer Science. 185, 139-151 https://doi.org/10.1016/j.entcs.2007.05.034
  12. John, R., Yan, Li and Landers, T. L. (1996) A modified bathtub curve with latent failures. Microelectronics and Reliability. 36, 217-220
  13. Huang, C., Y. and Chang, Y., R. (2007) An improved decomposition scheme for assessing the reliability of embedded systems by using dynamic fault trees. Reliability Engineering & System Safety. 92, 1403-1412 https://doi.org/10.1016/j.ress.2006.09.008
  14. Serra, J.,A., Domenech, E., Escriche, I. and Martorell, S. (1999) Risk assessment and critical control points from the production perspective. Inter. J. Food Microbiology. 46, 9-26 https://doi.org/10.1016/S0168-1605(98)00168-8