Salas Christian, Durán Orlando, Vergara José Ignacio, Arata Adolfo
Escuela de Ingeniería Mecánica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, Chile.
RMES Analytics, Santiago 7560742, Chile.
Entropy (Basel). 2024 Nov 8;26(11):961. doi: 10.3390/e26110961.
In a highly dynamic and complex environment where risks and uncertainties are inevitable, the ability of a system to quickly recover from disturbances and maintain optimal performance is crucial for ensuring operational continuity and efficiency. In this context, resilience has become an increasingly important topic in the field of engineering and the management of productive systems. However, there is no single quantitative indicator of resilience that allows for the measurement of this characteristic in a productive system. This study proposes the use of permutation entropy of ordinal patterns in time series as an indicator of resilience in industrial equipment and systems. Based on the definition of resilience, the developed method enables precise and efficient assessment of a system's ability to withstand and recover from disturbances. The methodology includes the identification of ordinal patterns and their analysis through the calculation of a permutation entropy indicator to characterize the dynamics of industrial systems. Case studies are presented and the results are compared with other resilience models existing in the literature, aiming to demonstrate the effectiveness of the proposed approach. The results are promising and highlight a highly applicable and simple indicator for resilience in industrial systems.
在一个风险和不确定性不可避免的高度动态且复杂的环境中，系统从干扰中快速恢复并维持最佳性能的能力对于确保运营连续性和效率至关重要。在此背景下，恢复力已成为工程领域和生产系统管理中日益重要的话题。然而，目前尚无单一的定量指标可用于衡量生产系统中的恢复力这一特性。本研究提出将时间序列中有序模式的排列熵用作工业设备和系统恢复力的指标。基于恢复力的定义，所开发的方法能够精确且高效地评估系统承受干扰并从中恢复的能力。该方法包括识别有序模式，并通过计算排列熵指标对其进行分析，以表征工业系统的动态特性。文中给出了案例研究，并将结果与文献中现有的其他恢复力模型进行了比较，旨在证明所提方法的有效性。结果很有前景，并突出了一种在工业系统中高度适用且简单的恢复力指标。
