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Using Fuzzy-Improved Principal Component Analysis (PCA-IF) for Ranking of Major Accident Scenarios

  • Hadef HefaidhEmail author
  • Djebabra Mébarek
Research Article - Systems Engineering
  • 11 Downloads

Abstract

The industrial risk mapping is a topical problem in the field of risk management that attracts many researchers to develop risk matrices to ensure consultation between their actors. In this context, this paper aims to propose the principal component analysis (PCA) method as support for this consultation. Indeed, the use of PCA method is justified by its robustness for aggregate initial data associated with industrial risks as principal factors and ranking of this risk in terms of their criticalities in risk matrices. However, the aggregation of initial data on industrial risks by the main factors, in some cases, leads to inaccuracies which make it difficult to classify certain risks. This paper proposes two variants of PCA method to solve this inaccuracy and succeeds in classifying risks according to their respective criticalities, namely PCA-Improved (PCA-I) and PCA-I-Fuzzy (PCA-IF). The results come from the PCA application and its proposed variants (PCA-I and PCA-IF) on an example of accident scenarios ranking. We have established a scientific basis for the capitalization of mapping tool for consultation and decision support to industrial risk managers.

Keywords

Major accident scenarios Risk ranking PCA Fuzzy logic Improved PCA 

Notes

Acknowledgments

We thank the anonymous reviewers for comments that greatly improved the manuscript substantially.

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Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.LRPI Laboratory – IHSI InstituteUniversity of Batna 2BatnaAlgeria
  2. 2.Applied Engineering Department, Institute of TechnologyUniversity of OuarglaOuarglaAlgeria

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