AI military vehicle diagnostics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00565896" target="_blank" >RIV/60162694:G43__/26:00565896 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11061248" target="_blank" >http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11061248</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICMT65201.2025.11061275" target="_blank" >10.1109/ICMT65201.2025.11061275</a>

Jazyk výsledku

angličtina

Název v původním jazyce

AI military vehicle diagnostics

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

AI military vehicle diagnostics

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 10th International Conference on Military Technologies, ICMT 2025 - Proceedings

ISBN

979-8-3315-2338-1

ISSN

—

e-ISSN

2996-4474

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Brno Czech Republic

Místo konání akce

Brno, Czech Republic

Datum konání akce

27. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001545807300018