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Related Experiment Videos

Using expert models in human reliability analysis-a dependence assessment method based on fuzzy logic.

Luca Podofillini¹, Vinh Dang, Enrico Zio

¹Department of Nuclear Energy and Safety, Paul Scherrer Institute, Villigen PSI, Switzerland.

Risk Analysis : an Official Publication of the Society for Risk Analysis

|May 26, 2010

Summary

This summary is machine-generated.

This study introduces a fuzzy expert system for human reliability analysis (HRA) dependence assessment. It makes expert rules explicit and computable, improving repeatability in operator performance analysis.

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Area of Science:

Human reliability analysis
Expert systems
Cognitive modeling

Background:

Dependence analysis in HRA assesses task failure influence on subsequent tasks.
Current methods like THERP and decision trees have limitations in subjectivity and traceability.
There is a need for a more objective and transparent method for dependence assessment.

Purpose of the Study:

To develop a novel method for dependence assessment in HRA.
To capture and formalize expert knowledge into a computable algorithm and software tool.
To enhance the objectivity, repeatability, and traceability of HRA dependence analysis.

Main Methods:

Development of a fuzzy expert system (FES) for dependence assessment.
Systematic elicitation of expert rules for dependence evaluation.
Creation of a traceable, explicit, and computable model based on expert knowledge.
Demonstration using a working model with anchor situations for guidance.

Main Results:

A fuzzy expert system-based method for HRA dependence assessment was developed.
Expert rules were systematically elicited and converted into a traceable, computable model.
The method provides guidance through anchor situations, enhancing analyst judgment.
The FES approach offers a usable and repeatable way to access expert rules.

Conclusions:

The FES-based method provides a more objective, repeatable, and traceable approach to HRA dependence assessment.
This method effectively formalizes expert knowledge, making it accessible to HRA analysts.
The developed tool supports improved decision-making in human reliability analysis.