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Liang, Zhengyu, Arnaldo Valdes, Rosa Maria ORCID: https://orcid.org/0000-0001-6639-6819, Gomez Comendador, Victor Fernando
ORCID: https://orcid.org/0000-0003-0961-2188 and Saez Nieto, Francisco Javier
(2019).
Detection of common causes between air traffic serious and major incidents in applying the convolution operator to Heinrich Pyramid Theory.
"Entropy", v. 21
(n. 12);
pp. 1166-1198.
ISSN 1099-4300.
https://doi.org/10.3390/e21121166.
Title: | Detection of common causes between air traffic serious and major incidents in applying the convolution operator to Heinrich Pyramid Theory |
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Author/s: |
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Item Type: | Article |
Título de Revista/Publicación: | Entropy |
Date: | November 2019 |
ISSN: | 1099-4300 |
Volume: | 21 |
Subjects: | |
Freetext Keywords: | Heinrich’s pyramid theory; convolutional matrix; ATM incident analysis; information theory; aviation safety |
Faculty: | E.T.S. de Ingeniería Aeronáutica y del Espacio (UPM) |
Department: | Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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Heinrich’s pyramid theory is one of the most influential theories in accident and incident prevention, especially for industries with high safety requirements. Originally, this theory established a quantitative correlation between major injury accidents, minor injury accidents and no-injury accidents. Nowadays, researchers from different fields of engineering also apply this theory in establishing quantitatively the correlation between accidents and incidents. In this work, on the one hand, we have detected the applicability of this theory by studying incident reports of different severities occurred in air traffic management. On the other hand, we have deepened the analysis of this theory from a qualitative perspective. For this purpose, we have applied the convolution operator in identifying correlations between contributing causes to different incident severities, also known as precursors to accidents, and system failures. The results suggested that system failures are mechanisms by which the causes are manifested. In particular, the same underlying cause can be manifested through different failures which contribute to incidents with different severities. Finally, deriving from this result, an artificial neuronal network model is proposed to recognize future causes and their possible associated incident severities.
Item ID: | 57783 |
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DC Identifier: | https://oa.upm.es/57783/ |
OAI Identifier: | oai:oa.upm.es:57783 |
DOI: | 10.3390/e21121166 |
Official URL: | https://www.mdpi.com/1099-4300/21/12/1166 |
Deposited by: | Memoria Investigacion |
Deposited on: | 12 Mar 2021 06:31 |
Last Modified: | 12 Mar 2021 06:31 |