Risk prediction for modern technological systems

• Autorzy: Duffey R. B. , Saull J. W.
• Języki publikacji: EN

Abstrakty

EN

We have already examined the worldwide trends for outcomes (measured as accidents, errors and events) using data available for large complex technological systems with human involvement. That analysis was a dissection of the basic available, published data on real and measured risks, for trends and inter-comparisons of outcome rates. We found and showed how all the data agreed with the learning theory when the accumulated experience is accounted for. Here, learning includes both positive and negative feedback, directly or indirectly, as a result of prior outcomes or experience gained, in both the organizational and individual contexts. Our purpose here and now is to try to introduce some predictability and insight into the risk or occurrence of these apparently random events. In seeking such a general risk prediction we adopt a fundamental theoretical approach that is and must be testable against the world’s existing data. Comparisons with outcome error data from the world’s commercial airlines, the two shuttle failures, and from nuclear plant operator transient control behaviour, show a reasonable level of accord. The results demonstrate that the risk is dynamic, and that it may be predicted using the MERE learning hypothesis and the minimum failure rate, and can be utilized for predictive risk analysis purposes.

Słowa kluczowe

EN

technological systems; risk; outcome; failure; error; events; probability

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2007
• Tom: Vol. 1
• Strony: 75--81
• Opis fizyczny: Bibliogr. 6 poz., wykr.

Twórcy

autor

Duffey R. B.

• Atomic Energy of Canada Limited, Chalk River, ON, Canada

autor

Saull J. W.

• International Federation of Airworthiness, East Grinstead, UK

Bibliografia

• [1] Airsafe (2000). Fatal Events and Fatal Event Rates from 1970-2000, September, http://www.airsafe.com.
• [2] Duffey, R.B. & Saull, J.W. (2002). Know the Risk. First Edition, Butterworth and Heinemann, Boston, USA.
• [3] Fang, Christina. (2003). Stern School of Business, New York. Learning in the absence of feedback, unpublished MS.
• [4] NASA. Implementation Plan for Return to Flight, National Aeronautics and Space Administration, http://www1.nasa.gov).
• [5] Ohlsson, S. (1996). Learning from Performance Errors. Psychological Review, Vol. 103, No. 2, 241-262.
• [6] Sveshnikov, A.A. (1968). Problems in Probability Theory, Mathematical Statistics and the Theory of Random Functions. Dover, New York.