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Fuzzy Systems in Use for Human Reliability Analysis

Learn about the use of Fuzzy Logic in Human Reliability Analysis with the CREAM methodology for accurate assessments. Explore methodology, results, and conclusions.

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Fuzzy Systems in Use for Human Reliability Analysis

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  1. Fuzzy Systems in Use for Human Reliability Analysis Myrto Konstandinidou Zoe Nivolianitou Nikolaos Markatos Christos Kyranoudis Loss PreventionPrague, June 2004

  2. Outline • Introduction • The Fuzzy Logic as a modeling tool • Methods for Human Reliability Analysis • The CREAM methodology • Development of the Fuzzy Classification System • Results • Conclusions

  3. Introduction • HRA is a critical element for PRA • Most important concerns: - the subjectivity of the methods - the uncertainty of data - the complexity of the human factor per se • Fuzzy logic theory has had many relevant applications in the last years

  4. Fuzzy Logic as a modeling tool (1) • Fuzzy logic (FL) is a very useful tool for modeling - complex systems - qualitative, inexact or uncertain information • FL resembles the way humans make inference and take decisions • FL accommodates ambiguities of real world human language and logic

  5. Fuzzy Logic as a modeling tool (2) • Applications - Automatic control - Data classification - Decision analysis - Computer Vision - Expert systems The most used fuzzy inference method: Mamdani’s method (1975)

  6. Fuzzy Logic as a modeling tool (3) • Definitions FL allows an object to be a member of more that one sets and to partially belong to them. - Fuzzy set - Degree of membership - Partial membership

  7. Fuzzy Logic as a modeling tool (4) • The 3 steps of a FL system Fuzzification: the process of decomposing input variables to fuzzy sets Fuzzy Inference: a method to interpret the values of the input vectors Defuzzification: the process of weighting and averaging the outputs Fuzzification Defuzzification Crisp Output Crisp Input Inference

  8. Methods of Human Reliability Analysis • Fundamental Limitations • Insufficient data • Methodological limitations • Uncertainty • Most important methods developed for HRA: • THERP • CREAM • ATHEANA

  9. CREAM Methodology (1) The choice of CREAM was made because: • It is well structured and precise • It fits better in the general structure of FL • It presents a consistent error classification system • This system integrates individual, technological and organizational factors

  10. CREAM Methodology (2) Control Modes • Scrambled • Opportunistic • Tactical • Strategic Definition of Common Performance Conditions (CPCs) to be used in FL model

  11. Experience - Accident analysis - Risk assessment - Human reliability Data - Diagrams of CREAM - MARS Database - Incidents and accidents from the Greek Petrochemical Industry Development of a Fuzzy Classifier (1)

  12. STEP 1 Selection of input parameters STEP 3 Development of the Fuzzy Rules STEP 2 Development of the Fuzzy sets Development of a Fuzzy Classifier (2) The Development of the Fuzzy Classification System for Human Reliability Analysis

  13. Development of a Fuzzy Classifier (3) STEP 1: Selection of the input parameters

  14. Development of a Fuzzy Classifier (4) STEP 2: Development of the Fuzzy sets • Each input is given a number based on its quality 0 (worst case) - 100 (best case) • “Time of day” from 0:00 (midnight) to 24:00 • Output scale 0.5*10-5 - 1.0*100

  15. Development of a Fuzzy Classifier (5)

  16. Development of a Fuzzy Classifier (6) Output fuzzy sets: Probability of a human erroneous action

  17. Development of a Fuzzy Classifier (7) Input variable

  18. Development of a Fuzzy Classifier (8) Action Failure Probability 1 0 -5.30E+00 -4.30E+00 -3.30E+00 -2.30E+00 -1.30E+00 -3.00E-01 Strategic Probability interval Output Tactical Opportunistic Scrambled

  19. Development of a Fuzzy Classifier (9) STEP 3: Development of the fuzzy rules • Based on CREAM basic diagram • Simple linguistic terms • Logical AND operation

  20. Σ improved reliability 7 . 6 5 4 3 2 1 Σ 1 2 3 4 5 6 7 8 9 reduced reliability Strategic Tactical Opportunistic Scrambled CREAM basic diagram

  21. Fuzzification Defuzzification Probability that operator performs erroneous action Input values Inference Development of a Fuzzy Classifier (10) Fuzzy model operations

  22. Scenarios Five independent scenarios characterizing 5 different industrial contexts: • Scenario 2 represents a best case scenario • Scenario 4 represents a worst case scenario • Scenarios 4 and 5 have slight differences in the values of input parameters

  23. Fuzzy Model results 1.0*10-2 9.81*10-4 6.33*10-2 2.02*10-1 1.91*10-1 Results of test runs Scenario Control Mode Probability interval 1 1.0*10-3<p<1.0*10-1 Tactical 2 (Best case) 0.5*10-5<p<1.0*10-2 Strategic 3 1.0*10-2<p<0.5*100 Opportunistic 4 (Worst case) 1.0*10-1<p<1.0*100 Scrambled 5 1.0*10-1<p<1.0*100 Scrambled

  24. Comments on the results • All FL model results in accordance with CREAM • Best case scenario very low action failure probability • Worst case scenario very high action failure probability • Small differences in input have impact to output • The results can be used directly in PSA methods (event trees, fault trees, etc.)

  25. Conclusions (1) FL system to estimate the probability of human erroneous action has been developed: • Based on CREAM methodology • 9 input variables • 1 output parameter

  26. Conclusions (2) • Test runs for 5 different scenarios • Very satisfactory results • Main difference between FL model and CREAM: probabilities estimation are exact numbers • The results can and will be used in other PSA methods

  27. Further goals • Model calibration with data from the Greek Petrochemical Industry • Addition of other CPCs or PSFs • Expansion to other fields of the chemical industry • Application in other fields of technology (e.g aviation technology, maritime transports, etc…)

  28. Acknowledgments The Financial support of the EU Commission through project “PRISM” GTC1-2000-28030 to this research is kindly acknowledged

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