Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/6967
Título: Entropy Analysis of Industrial Accident Data Series
Autor: Lopes, António M.
Machado, J. A. Tenreiro
Palavras-chave: Entropy
Data: 2015
Editora: ASME
Relatório da Série N.º: Journal of Computational and Nonlinear Dynamics;Vol. 11, Issue 3
Resumo: 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.
URI: http://hdl.handle.net/10400.22/6967
DOI: 10.1115/1.4031195
Versão do Editor: http://computationalnonlinear.asmedigitalcollection.asme.org/article.aspx?articleid=2423818
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