Fusing model-based and data-driven prognostic methods for real-time model updating
Tianzhi Li (KTH Royal Institute of Technology, Digital Futures)
Morteza Moradi (TU Delft - Group Rans)
Michel Gokan Khan (KTH Royal Institute of Technology, Digital Futures)
Renan Guarese (Digital Futures, KTH Royal Institute of Technology)
Jan Kronqvist (KTH Royal Institute of Technology)
Mario Romero (Linköping University)
Ming Xiao (KTH Royal Institute of Technology)
Xi Vincent Wang (KTH Royal Institute of Technology)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Prognostic methods broadly fall into two categories—model-based and data-driven—both of which have shown effectiveness across a range of engineering applications. Model-based approaches require an explicit representation of the degradation process, defining failure as the point when the physical damage state exceeds a predetermined threshold. Data-driven methods, on the other hand, leverage sensor data to directly predict end-of-life (EOL) or related prognostic information. Although both approaches offer insights that could be complementary and potentially fused, most existing fusion methods either combine the outputs from multiple methods or adopt a data-driven method to assist the model-based method. To further enhance the prognostic performance, this study proposes a fusion-based prognostic approach in which the output of one method is actively used to update the model of the other through either the crossover operator or the likelihood function. The proposed approach is validated using both an aluminum fatigue dataset and the Prognostics and Health Management (PHM) 2010 cutter wear dataset, demonstrating improved prognostic accuracy compared to either method used independently.
help_outline