Digital twin approach for intelligent operation planning and health management of mechanical systems

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Nov 3, 2020
Pranav Karve Yulin Guo Berkcan Kapusuzoglu Sankaran Mahadevan Mulugeta Haile

Abstract

The digital twin paradigm aims to fuse information obtained from sensor data, physics models, and operational data for a mechanical component in use to make well-informed decisions regarding health management and operations of the component. In this work, we discuss a methodology for digital-twin-based operation planning in mechanical systems to enable: a) cost-effective maintenance scheduling, and b) resilient operations of the system. As properties of mechanical systems, as well as their operational parameters, loads and environment are stochastic in nature, our methodology includes probabilistic damage diagnosis, probabilistic damage prognosis, and system optimization under uncertainty. As an illustrative example, we consider the problem of fatigue crack growth in a metal component. We discuss a probabilistic, ultrasonic-guided-wave-based crack diagnosis framework that can handle both aleatory and epistemic uncertainties in the diagnosis process. We build a high-fidelity, finite element model to simulate the piezoelectric effect and ultrasonic guided wave propagation. We use test data obtained by conducting diagnostic experiments on the physical twin to calibrate the error in the diagnosis model. We perform Bayesian diagnosis of crack growth using the corrected diagnosis model, considering data corrupted by measurement noise, and fuse the information from multiple sensors. We build a finite-element-based high-fidelity model for crack growth under uniaxial cyclic loading, and calibrate a phenomenological (low-fidelity) fatigue crack growth model using the high-fidelity model output as well as data from fatigue loading experiments performed on the physical twin. We use the resulting multi-fidelity model in a probabilistic crack growth prognosis framework; thus achieving both accuracy and computational efficiency. Lastly, we utilize the damage diagnosis framework along with the damage prognosis model to optimize system operations under diagnostic and prognostic uncertainty. We perform simulation as well as laboratory experiments that show how the digital twin of the component of interest can be used for intelligent health management and operation planning for mechanical systems.

How to Cite

Karve, P., Guo, Y., Kapusuzoglu, B., Mahadevan, S., & Haile, M. (2020). Digital twin approach for intelligent operation planning and health management of mechanical systems. Annual Conference of the PHM Society, 12(1), 9. https://doi.org/10.36001/phmconf.2020.v12i1.1197
Abstract 46 | PDF Downloads 95

##plugins.themes.bootstrap3.article.details##

Keywords

Information fusion, probabilistic damage diagnosis, probabilistic damage diagnosis, fatigue crack growth, optimization under uncertainty, digital twin

Section
Technical Papers

DB Error: Unknown column 'Array' in 'where clause'