AI military vehicle diagnostics

This paper deals with the possibility of diagnosis and prediction of military vehicle failures using machine learning to increase the reliability of military equipment operation and early detection of impending failure. Early detection of incipient faults is absolutely crucial for timely repair planning and thus elimination of downtime in vehicle operations. First, we propose an appropriate method of data acquisition from vehicles considering the cost and efficiency of exploitation. We then propose a suitable approach to fault diagnosis for engine, transmission and brakes using vibration and acoustic diagnostics. These models identify anomalies in engine sound and vibration in the transmission and brake system. In the future, we plan to use these findings to develop a suitable model for machine learning.This study presents an effective solution for real-time acoustic monitoring, which is particularly beneficial for remote control and all-weather monitoring, which is then used for evaluation and scheduling of service operations on individual vehicles. This system is equally applicable to current and older equipment.