Entropy Analysis of Industrial Accident Data Series

• Tipo: Artigo em Revista Científica Internacional
• Data: 2016

Autores

António Mendes Lopes (Autor) FEUP; Tenreiro Machado, JAT (Autor) Outra

Revista

Título: Journal of Computational and Nonlinear Dynamics

Vol. 11

ISSN: 1555-1423

Editora: ASME

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 6 Citações

Outras Informações

ID Authenticus: P-00G-T4B

DOI: 10.1115/1.4031195

Abstract (EN): Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback-Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback-Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7