Physics Informed Machine Learning for Advanced Diagnostics & Prognostics of Ground Combat Vehicles

2024-01-4089

09/16/2024

Features
Event
2024 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium
Authors Abstract
Content
We introduce novel approaches utilizing Physics Informed Machine Learning (PIML) for advanced diagnostics & prognostics of ground combat vehicles (CV). Specifically, we present the development of a PIML model designed to predict the health of engine oil in diesel engines. The condition of engine oil is closely linked to engine wear, thus serving as a crucial indicator of engine health. Our model integrates a physics-based simulation of engine wear in diesel engines, leveraging a time history of engine oil viscosity and engine speed as key input parameters. Furthermore, we conduct uncertainty quantification to assess the impact of varying parameters on engine oil health prediction. Additionally, our model demonstrates the capability to enhance low-fidelity physics models through the integration of a limited set of experimental data. By combining data-driven techniques with physics-based insights, our approach offers enhanced diagnostics and prognostics capabilities for ground combat vehicles, thereby facilitating proactive maintenance and optimization for operational readiness.
Meta TagsDetails
DOI
https://doi.org/10.4271/2024-01-4089
Pages
15
Citation
Betts, J., and Alizadeh, A., "Physics Informed Machine Learning for Advanced Diagnostics & Prognostics of Ground Combat Vehicles," SAE Technical Paper 2024-01-4089, 2024, https://doi.org/10.4271/2024-01-4089.
Additional Details
Publisher
Published
Sep 16
Product Code
2024-01-4089
Content Type
Technical Paper
Language
English