Prescriptive maintenance for military vehicles using frugal learning

In the realm of military operations, the reliability and operational readiness of vehicles are paramount. Unlike predictive maintenance, which forecasts potential failures, prescriptive maintenance extends the analysis by recommending specific actions to optimize maintenance schedules and enhance operational effectiveness. This paper presents a novel approach to prescriptive maintenance aimed at estimating the remaining useful life of military vehicle components. Three key challenges for achieving precise maintenance planning for operational awareness are: (i) limited availability of training data, (ii) the diversity of vehicle platforms and components, and (iii) the need for rapid adaptation of pre-trained models to new platforms. These challenges are addressed through the integration of frugal learning, meta-learning, and transfer learning. To mitigate data scarcity, a digital twin is developed to simulate mission-related data across various heavy-duty vehicle platforms and mission profiles. While real-world data are used to construct and calibrate the digital twin, only the synthetic data it generates are utilized for model training. To tackle the second and third challenges, this paper evaluates and compares several frugal learning strategies based on transfer learning and meta-learning. The proposed learning strategies leverage the synthetic data to predict the remaining useful life of the components, such as fuel consumption, used as a representative case study, while remaining applicable to other critical components. Experimental results demonstrate that meta-learning offers a favorable trade-off between adaptation time and performance across platforms, whereas transfer learning achieves higher overall prediction accuracy at the cost of increased fine-tuning time. The digital twin, which is constructed using OpenStreetMap-based trajectories, enables rapid adaptation to novel environments, thereby enhancing mission planning and vehicle readiness. This work contributes to a scalable, adaptable, and cost-effective AI-driven maintenance solution for defense applications, capable of supporting diverse operational scenarios with minimal data requirements.