Enhancing Multiple Regression-based Resilience Model Prediction with Transfer Function  
Author Fatemeh Salboukh

 
Co-Author(s) Priscila Silva; Mindy Hotchkiss; Lance Fiondella

 
Abstract Resilience engineering involves creating and maintaining systems capable of efficiently managing disruptive incidents. Past research in this field has employed various statistical techniques to track and forecast the system’s recovery process within the resilience curve. However, many of these techniques fall short in terms of flexibility, struggling to accurately capture the details of shocks. Moreover, most of them are not able to predict long-term dependencies. To address these limitations, this paper introduces an advanced form of time series modeling, that allows for the incorporation of explanatory variables vs standard time series, the transfer function, which effectively tracks and predicts changes in system performance when subjected to multiple shocks and stresses of varying intensity and duration. This approach offers a structured methodology to plan resilience assessment tests tailored to specific shocks and stresses as well as guide the necessary data collection to ensure efficient test execution. Although resilience engineering is domain-specific, the transfer function is a versatile approach, making it suitable for various domains. To assess the effectiveness of the transfer function model, we conduct a comparative analysis with the regression model with interaction, using historical data on job losses during the 1980 recessions in the United States. This comparison not only underscores the strengths of the transfer function in handling complex temporal data but also reaffirms its competitiveness compared to existing methods. Our numerical results using goodness of fit measures provide compelling evidence of the transfer function model’s enhanced predictive power, offering an alternative approach to resilience assessment.

 
Keywords Resilience engineering, transfer function, time series, detection of system degradation, recovery time estimation, long-term predictions
   
    Article #:  RQD2024-11
 

Proceedings of 29th ISSAT International Conference on Reliability & Quality in Design
August 8-10, 2024