MILPDM: an architecture for predictive maintenance of assets in the military domain

Abstract

Predictive maintenance is a topic addressed in different contexts such as industry, logistics, and healthcare, where sensing equipment parameters allows monitoring health degradation and anticipating failures. This same approach has been used in the military, monitoring assets such as vehicles. Monitoring asset degradation generates economic benefits similar to those observed in other areas, reducing costs by optimizing the use of monitored assets. However, assets operating in the military domain perform critical tasks, where failures can generate a high material and human cost. Applying predictive maintenance to military equipment is challenging. Vehicles and equipment are sent for missions in already known scenarios with high availability of collected data. However, this equipment can be sent to new environments where there is no data from previous operations to evaluate the degradation of the equipment’s health. The approaches in the literature for failure prediction applied to the military domain focus on equipment monitoring. This work presents a broader approach through the use of MILPdM. This architecture aims to predict failures applied to the military domain, considering the dynamic scenario in which military equipment operates. Our approach has two distinct fronts. First, we seek to verify the possibility of using machine learning models to predict failures, and from that point on, we seek to verify the prediction capacity in new scenarios, where we test the new foundation models for predicting lifespan in new scenarios, with a comparison of results with traditional models. We propose four use cases to test the architecture. Two use cases validate traditional failure prediction models using long-short term memory and random forest machine learning algorithms. Two other use cases evaluate the use of foundation models in new scenarios. The results acquired from the prediction of the trained models show that MILPdM can anticipate failures with high accuracy. As for the prediction capacity in new scenarios, using the foundation model proved promising, surpassing traditional learning models. These results show the potential of using the foundation model in predictive maintenance.