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Permutation Entropy: An Ordinal Pattern-Based Resilience Indicator for Industrial Equipment.

Christian Salas1, Orlando Durán1, José Ignacio Vergara2

  • 1Escuela de Ingeniería Mecánica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, Chile.

Entropy (Basel, Switzerland)
|November 27, 2024
PubMed
Summary

This study introduces permutation entropy of ordinal patterns as a novel, quantitative indicator for measuring the resilience of industrial systems. This method effectively assesses a system's capacity to withstand and recover from operational disturbances.

Keywords:
industrial resilienceordinal patternspermutation entropyresilience assessment

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Area of Science:

  • Engineering
  • Industrial Systems Management
  • Complex Systems Analysis

Background:

  • Operational continuity and efficiency in dynamic environments necessitate robust system resilience.
  • Existing quantitative resilience indicators for productive systems are lacking.
  • Resilience is critical for managing risks and uncertainties in industrial operations.

Purpose of the Study:

  • To propose permutation entropy of ordinal patterns in time series as a quantitative indicator for resilience in industrial equipment and systems.
  • To develop a precise and efficient method for assessing system resilience based on its ability to withstand and recover from disturbances.
  • To validate the proposed indicator through case studies and comparison with existing resilience models.

Main Methods:

  • Utilizing permutation entropy of ordinal patterns from time series data.
  • Identifying and analyzing ordinal patterns to characterize industrial system dynamics.
  • Calculating a permutation entropy indicator to quantify system resilience.

Main Results:

  • The proposed permutation entropy method provides a precise and efficient assessment of industrial system resilience.
  • Case studies demonstrate the effectiveness of the permutation entropy indicator in evaluating a system's response to disturbances.
  • The results show promising applicability and simplicity compared to other resilience models.

Conclusions:

  • Permutation entropy of ordinal patterns offers a valuable and simple quantitative indicator for resilience in industrial systems.
  • The developed methodology enhances the assessment of operational continuity and efficiency under uncertainty.
  • This approach contributes a practical tool for engineers and managers in optimizing system performance and recovery.