Assessing the Risks of Airport Airside through the Fuzzy Logic-Based Failure Modes, Effect, and Criticality Analysis

Cheng-Min Feng,Chi-Chun Chung

doi:10.1155/2013/239523

Abstract

To identify risk items, measure risk value objectively, and establish risk assessment matrix of airports is the major task of airport safety. This paper first extracts 14 risk items of airports from the International Civil Aviation Organization (ICAO) aviation accidents database and then applies Failure Modes, Effect and Criticality Analysis (FMECA) to define the decision factors of probability, severity and detectability of airport risks. This paper also designs a questionnaire and applies fuzzy logic to discover the importance of decision factors, to find out the threshold value of Risk Assessment Matrix, and to prioritize the airport risks. This paper uses Taiwan Taoyuan International Airport as a case study to demonstrate the modeling process and analyze the results.

Highlights

Any aviation accident may lead to unpredictable fatal losses
How to identify risk items, measure risk value objectively, and establish Risk Assessment Matrix of airports is a major task of airport safety management
Because of this incomplete information and data uncertainty, the traditional risk assessment ranks the level of risks through risk map based on the subjective experience and risk threshold value [3]

Summary

Introduction

Any aviation accident may lead to unpredictable fatal losses. Statistically, almost 80% of aviation accidents occur at airports. How to identify risk items, measure risk value objectively, and establish Risk Assessment Matrix of airports is a major task of airport safety management. Quantitative assessment of risk is challenging in aviation safety domain where undesired events are extremely rare, and the causal factors are difficult to quantify and nonlinearly related [2]. Because of this incomplete information and data uncertainty, the traditional risk assessment ranks the level of risks through risk map based on the subjective experience and risk threshold value [3]. Since the subjective experience involves fuzzy linguistic variables to describe the severity, frequency of occurrence of the failure, and their fuzzy relationship, fuzzy logic based on the experience of experts is a good method to deal with risk assessment

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