Abstract

The need for condition-based maintenance and sustainability management of military assets has become increasingly important due to reductions in military personnel and budget constraints. In this context, research is actively being conducted using sensors that collect environmental data to determine optimal inspection intervals and identify vulnerabilities early. This study proposes an algorithm to dynamically adjust the sampling interval based on the rate of change in measured physical quantities to reduce sensor battery consumption. Various environmental sensing data, such as temperature, humidity, acceleration, and pressure, were collected from a simulation device that mimicked the storage environment of actual guided missiles. The analysis of the collected data revealed that setting the RSME threshold within 5 improved battery lifespans by 11% compared to the original data, while demonstrating that the temperature RMSE was estimated to improve by about 4.76 in adaptive sampling and 5.44 in uniform sampling. This demonstrates that our approach may improve maintenance efficiency by monitoring military assets more effectively and for extended periods.