Download Fuzzy Systems in Use for Human Reliability Analysis

Fuzzy Systems in Use for Human
Reliability Analysis
Myrto Konstandinidou
Zoe Nivolianitou
Nikolaos Markatos
Christos Kyranoudis
Loss Prevention Prague, June 2004
NCSR “DEMOKRITOS”
Institute of Nuclear Technology
and Radiation Protection
NATIONAL TECHNICAL
UNIVERSITY OF ATHENS
School of Chemical Engineering
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
NCSR “DEMOKRITOS”
Institute of Nuclear Technology
and Radiation Protection
NATIONAL TECHNICAL
UNIVERSITY OF ATHENS
School of Chemical Engineering
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
NCSR “DEMOKRITOS”
Institute of Nuclear Technology
and Radiation Protection
NATIONAL TECHNICAL
UNIVERSITY OF ATHENS
School of Chemical Engineering
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
NCSR “DEMOKRITOS”
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and Radiation Protection
NATIONAL TECHNICAL
UNIVERSITY OF ATHENS
School of Chemical Engineering
Fuzzy Logic as a modeling tool (2)

Applications
- Automatic control
- Data classification
The most used fuzzy
inference method:
Mamdani’s method
(1975)
- Decision analysis
- Computer Vision
- Expert systems
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NATIONAL TECHNICAL
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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
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NATIONAL TECHNICAL
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Fuzzy Logic as a modeling tool (4)

The 3 steps of a FL system
Fuzzification
Crisp Input
Defuzzification
Inference
Crisp Output
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
NCSR “DEMOKRITOS”
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NATIONAL TECHNICAL
UNIVERSITY OF ATHENS
School of Chemical Engineering
Methods of Human Reliability Analysis

Fundamental Limitations
–
–
–

Insufficient data
Methodological limitations
Uncertainty
Most important methods developed for HRA:
–
–
–
THERP
CREAM
ATHEANA
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CREAM Methodology (1)
The choice of CREAM was made because:
1)
It is well structured and precise
2)
It fits better in the general structure of FL
3)
It presents a consistent error classification system
4)
This system integrates individual, technological and
organizational factors
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NATIONAL TECHNICAL
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CREAM Methodology (2)
Control Modes
1.
Scrambled
2.
Opportunistic
3.
Tactical
4.
Strategic
Definition of Common Performance Conditions
(CPCs) to be used in FL model
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NATIONAL TECHNICAL
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Development of a Fuzzy Classifier (1)
Experience
- Accident analysis
- Risk assessment
- Human reliability
Data
- Diagrams of CREAM
- MARS Database
- Incidents and accidents from
the Greek Petrochemical
Industry
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Institute of Nuclear Technology
and Radiation Protection
NATIONAL TECHNICAL
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Development of a Fuzzy Classifier (2)
The Development of the Fuzzy Classification
System for Human Reliability Analysis
STEP 1
Selection of input
parameters
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STEP 2
Development of
the Fuzzy sets
STEP 3
Development of
the Fuzzy Rules
NATIONAL TECHNICAL
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Development of a Fuzzy Classifier (3)
STEP 1: Selection of the input parameters
Adequacy of
organization
Number of
simultaneous
goals
Crew
collaboration
quality
Working
conditions
Available time
Adequacy of
training
Adequacy of
maintenance &
support
Availability of
procedures &
plans
Time of day
(Circadian
rhythm)
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NATIONAL TECHNICAL
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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
NCSR “DEMOKRITOS”
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NATIONAL TECHNICAL
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Development of a Fuzzy Classifier (5)
CPCs
INPUT
Fuzzy sets
Adequacy of organization
4
Working conditions
3
Availability of procedures
3
Adequacy of maintenance
4
No of simultaneous goals
3
Available time
3
Time of day
3
Adequacy of training
3
Crew collaboration quality
4
OUTPUT Probability of human erroneous action
4
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NATIONAL TECHNICAL
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Development of a Fuzzy Classifier (6)
Output fuzzy sets:
Probability of a human erroneous action
Control mode
Strategic
Action failure
probability
0.5*10-5<p<1.0*10-2
Tactical
1.0*10-3<p<1.0*10-1
Opportunistic
1.0*10-2<p<0.5*100
Scrambled
1.0*10-1<p<1.0*100
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NATIONAL TECHNICAL
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Development of a Fuzzy Classifier (7)
Quality o f Working Con ditions
1
0
0
10
20
30
40
50
60
W orking conditions
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70
80
90
100
Incom patible
Com patible
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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
Tactical
Opportunistic
Output NCSR “DEMOKRITOS”
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Development of a Fuzzy Classifier (9)
STEP 3: Development of the fuzzy rules

Based on CREAM basic diagram

Simple linguistic terms

Logical AND operation
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NATIONAL TECHNICAL
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CREAM basic diagram
Σimproved
reliability
7.
6
5
4
3
2
1
1 2 3 4 5 6 7 8 9
Σreduced
reliability
Strategic
Tactical
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Opportunistic
Scrambled
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Development of a Fuzzy Classifier (10)
Fuzzy model operations
Fuzzification
Input values
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Defuzzification
Inference
Probability that
operator performs
erroneous action
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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
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NATIONAL TECHNICAL
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Results of test runs
Scenario Control
Mode
1
Tactical
Probability
Fuzzy Model
results
interval
1.0*10-3<p<1.0*10-1 1.0*10-2
2 (Best
case)
3
Strategic
0.5*10-5<p<1.0*10-2 9.81*10-4
Opportunistic 1.0*10-2<p<0.5*100
6.33*10-2
4 (Worst Scrambled
case)
1.0*10-1<p<1.0*100
2.02*10-1
5
1.0*10-1<p<1.0*100
1.91*10-1
Scrambled
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NATIONAL TECHNICAL
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Comments on the results

All FL model results in accordance with CREAM

Best case scenario
probability

Worst case scenario
probability

Small differences in input have impact to output

The results can be used directly in PSA methods
(event trees, fault trees, etc.)
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very low action failure
very high action failure
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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
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NATIONAL TECHNICAL
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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
NCSR “DEMOKRITOS”
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and Radiation Protection
NATIONAL TECHNICAL
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Further goals
1) Model calibration with data from the Greek
Petrochemical Industry
2) Addition of other CPCs or PSFs
3) Expansion to other fields of the chemical
industry
4) Application in other fields of technology
(e.g aviation technology, maritime transports,
etc…)
NCSR “DEMOKRITOS”
Institute of Nuclear Technology
and Radiation Protection
NATIONAL TECHNICAL
UNIVERSITY OF ATHENS
School of Chemical Engineering
Acknowledgments
The Financial support of the EU Commission
through project “PRISM” GTC1-2000-28030 to
this research is kindly acknowledged
NCSR “DEMOKRITOS”
Institute of Nuclear Technology
and Radiation Protection
NATIONAL TECHNICAL
UNIVERSITY OF ATHENS
School of Chemical Engineering