Entropy Analysis of Industrial Accident Data Series

• Type: Article in International Scientific Journal
• Date: 2016

Authors

António Mendes Lopes (Author) FEUP; Tenreiro Machado, JAT (Author) Other

Journal

Title: Journal of Computational and Nonlinear Dynamics

Vol. 11

ISSN: 1555-1423

Publisher: ASME

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 6 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7