STO-Activities: (no title)
Activity title:
Maintenance Modeling in UxV Design for Improved Readiness & Endurance
Activity Reference:
AVT-381
Panel:
AVT
Security Classification:
NATO UNCLASSIFIED
Status:
Active
Activity type:
RTG
Start date:
2024-01-01T00:00:00Z
Actual End date:
2026-12-31T00:00:00Z
Keywords:
conditionbased maintenance, data processing and management, design, inspection, maintenance modeling, monitoring, prognostic maintenance, repair, sustainment, Unmanned vehicles
Background:
Background to this activity is provided by AVT-355 Research Workshop on “Intelligent Solutions for Improved Mission Readiness of Military UxVs”, where four Working Groups (WG) met as part of the workshop. AVT-355 led to ET-219 “Maintenance Modeling in UxV Design for Improved Readiness and Endurance” based on the outcomes of AVT-355 WG#4 “Design Methodologies that will Optimize Maintenance Needs”, whose themes were (1) how should maintenance planning influence UxV design (including the types of sensors and machine learning/artificial intelligence approaches used, the level of redundancy, and the physical layout of the vehicle)? And (2) what sort of additional logistic support needs to be designed with the UxV to support rapid maintenance during operations? ET-219 confirmed interest in addressing these challenges via the proposed RTG.
Objectives:
The objective of the proposed activity is to conduct collaborations on maintenance modeling, focusing on addressing the technological gaps identified within AVT-355 and ATV-ET-219. To achieve this objective, two main areas of research will be addressed, namely (1) modeling of maintenance in design for effective monitoring and maintenance decisions and (2) design for prognostic maintenance including data processing and management. Developments will be closely coordinated with the follow-on effort from ET-217 “Dynamic Reconfigurable Mission Planning for Improved Readiness of Autonomous Military Vehicles Improved Readiness of Autonomous Military Vehicles”. This is to ensure onboard data-to-decision hardware and software components and systems are effectively integrated for maintenance and operational use.
Topics:
Studies on modeling of maintenance in design for effective monitoring and maintenance decisions will cover (a) modeling missions from the onset of the design to better track and plan for reliability, availability, maintainability, accounting for uncertainties, (b) use of redundant or different artificial intelligence and machine learning approaches to support robust decision-making, (c) possibly, modeling human factors in the maintenance problem.
Studies on design for prognostic maintenance including data processing and management will cover (a) developing/improving digital twins for design, including stochastic maintenance and mission models, and possibly considering/talking to environment digital twins, (b) developing the capability for nested models within models for digital twin, and (c) modeling and optimization of sensor deployment and data for monitoring and maintenance; exploring concepts from the computational science community, such as virtual sensing, active learning, and data fusion; and cost of data acquisition vs value to maintainers.
Properly defined maintenance models are deemed key to optimal monitoring of military vehicles, prognostic maintenance decisions, and finally design for maintenance. Their availability may be considered as potential breakthrough in these technological areas
Contact:
Open2Partners:
Title:
Created at 18/10/2022 14:00 by System Account
Last modified at 16/05/2024 10:00 by System Account
Go back to list
Home(NATO STO)