Detection of common causes between air traffic serious and major incidents in applying the convolution operator to Heinrich Pyramid Theory

Liang, Zhengyu and Arnaldo Valdes, Rosa Maria and Gomez Comendador, Victor Fernando 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.

Description

Title: Detection of common causes between air traffic serious and major incidents in applying the convolution operator to Heinrich Pyramid Theory
Author/s:
  • Liang, Zhengyu
  • Arnaldo Valdes, Rosa Maria
  • Gomez Comendador, Victor Fernando
  • Saez Nieto, Francisco Javier
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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Abstract

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.

More information

Item ID: 57783
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
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