著作 研究成果物
  • 安全とは
  • 会社案内
  • 業務案内
  • 実績と経歴
  • 報酬
  • 用語集
著作 研究成果物

著作 研究成果物

 ヒューマンエラー確率を考慮したリスクアセスメントツール(英文)、Safety Science, 2009

10-2009safetyscience.jpg
Tetsu Moriyama and Hideo Ohtani
Risk assessment tools incorporating human error probabilities in the Japanese small-sized establishment
Safety Science
journal homepage: www.elsevier.com/locate/ssci
2009 December Volume 47 Issue 10 Pages 1379-1397
Risk assessment; Tool; Unsafe act; PSF; Human error
Although it has been estimated that as many as 80% of all occupational accidents have human errors as a cause, no risk assessment tools incorporating human-related elements have been developed for small companies. Human error probability (HEP) and human error analysis (HEA) have been used for large-scale, safety-critical industries for last three decades, but these tools are not suitable for smaller, more general industries that comprise the majority of accident settings.
Here, we describe and verify a risk assessment tool that includes human-related elements for small companies. The tool expands on traditional risk assessment methods, such as matrix, risk graph and numerical scoring method, by adding human-related elements. The tool is easy-to-use in occupational environments, and includes assessments of human behavior and potentially outdated machinery at work place.
1. Introduction
2. Legal requirements for risk assessment
2.1. Background
2.2. Procedures
2.3. Requirements for conducting risk assessment in Japan
2.4. Unsafe acts in accident statistics
3. Human error and human error probability (HEP)
3.1. Human error
3.2. Causes of accidents and fault tree analysis (FTA) of unsafe acts
3.3. Human reliability estimation methods and sources
3.4. Typical HEP and PSF
3.5. PSF for risk assessment
3.6. PSF levels incorporating human error
4. Risk assessment
4.1. Definition of risk and elements
4.2. Tolerable risk
4.3. Risk estimation procedures and tools
5. Field study in the food processing industry
5.1. Entanglement risk for meat mixing machine5.2. Pullman loaf
slicer (sandwich bread slicer) and ‘‘cutting or severing” hazard
5.3. Human–machine interface example
6. Conclusions
1) American National Standards Institute, Inc., 2000. ANSI_B11.TR3:2000: Risk Assessment and Risk Reduction  A Guide to Estimate, Evaluate and Reduce Risks Associated with Machine Tools, ANSI, Washington, DC.
2) Benner Jr., L., 1985. Rating accident models and investigation methodologies. Journal of Safety Research 16 (3), 105 126.
3) Gertman, D., Blackman, H., et al., 2005. The SPAR-H Human Reliability Analysis Method. NUREG/CR-6883, NUREG, Washington, DC.
4) Gordon, R., Flin, R., et al., 2005. Designing and evaluating a human factors investigation tool (HFIT) for accident analysis. Safety Science 43 (3), 147 171.
5) Hashimoto, K., 1990. Safety Human Engineering. The Japan Industrial Safety and Health Association (JISHA), Tokyo. 6) Hollnagel, E., 1993. Cognitive Reliability and Error Analysis Method (CREAM). Elsevier Science, Amsterdam.
7) Hollywell, P.D., 1996. Incorporating human dependent failures in risk assessments to improve estimates of actual risk. Safety Science 22 (1 3), 177 194.
8) Johnson, C.W., 1996. Integrating human factors and systems engineering to reduce the risk of operator ‘‘error”. Safety Science 22 (1 3), 195 214.
9) Khan, F.I., Amyotte, P.R., et al., 2006. HEPI: a new tool for human error probability calculation for offshore operation. Safety Science, 22.
10) Kim, J.W., Jung, W., 2003. A taxonomy of performance influencing factors for human reliability analysis of emergency tasks. Journal of Loss Prevention in the Process Industries 16 (6), 16.
11) Kirwan, B., 1997a. Validation of human reliability assessment techniques Part-1  validation issues. Safety Science.
12) Kirwan, B., 1997b. Validation of human reliability assessment techniques part-2  validation results. Safety Science.
13) Neudorfer, A., 2002. Konstruieren Sicherheitsgerechter Produkte. Springer-Verlag, Heidelberg (Japanese edition).
14) National Aerospace Laboratory (NLR) Nederland, 2007. Safety Methods Database Version 0.7, NLR, Amsterdam.
15) Norman, D.A., 1981. Categorization of action slips. Psychological Review 88, 1 15.
16) Rasmussen, J., 1980. What Can be Learned from Human Error Reports? In: Duncan,
17) K.D., Gruenberg, M.M., Wallis, D. (Eds.). John Wiley and Sons Ltd., London.
18) Reason, J., 1990. Human Error. Cambridge University Press, New York.
19) Rigby, L.V., 1970. The nature of human error. In: Annual Technical Conference of the ASQC, American Society for Quality Control, Milwaukee, Wisconsin, 457 66.
20) Stanton, N., Baber, C., 1996. A systems approach to human error identification. Safety Science 22 (1-3), 215 228. 21) Stewart, M.G., Melchersand, R.E., 1997. Probabilistic Risk Assessment of Engineering Systems. Chapman and Hall, London, Berlin.
22) Swain, A.D., Guttman, H.E., 1983. Handbook of Human Reliability Analysis with Emphasis on Nuclear Power Plant Applications, NUREG/CR-1278, USREG, Washington, DC.
23) United States Nuclear Regulatory Commission, 1975. WASH-1400 Reactor Safety Study: An Assessment of Accident Risks in USA. Commercial Nuclear Power Plants, NUREG-75/014, USREG, Washington, DC.
24) United States Nuclear Regulatory Commission, 2000. Technical Basis and Implementation Guidelines for a Technique of Human Event Analysis (ATHEANA) NUREG-1624, Rev. 1, USREG, Washington, DC.
25) Williams, J., 1986. HEART  A proposed method for assessing and reducing human error. In: Ninth Advances in Reliability Technology Symposium, University of Bradford, NCRS, UK, pp. B3/R/1-B3/R/13.
26) Williams, J., 1985. Validation of human reliability assessment techniques. Reliability Engineering 11, 149 162.

 

pageTOPへ