Artificial Intelligence for Predictive Maintenance of Armoured Fighting Vehicles Engine
TSA Narayanan1, Suresh Chandra Padhy2
1Lt Gen TSA Narayanan, AVSM(Retd) Research Associate Poornima University, Ramchandrapura, P.O. Vidhani Vatika Sitapura Extention, Jaipur, Rajasthan, India.
2Dr Suresh Chandra Padhy, President (Vice Chancellor) Poornima University, Ramchandrapura, P.O. Vidhani Vatika Sitapura Extention, Jaipur, Rajasthan, India.
Manuscript received on 27 Jun 2023 | Revised Manuscript received on 12 July 2023 | Manuscript Accepted on 15 August 2023 | Manuscript published on 30 August 2023 | PP: 1-12 | Volume-3 Issue-5, August 2023 | Retrieval Number: 100.1/ijainn.E1071083523 | DOI: 10.54105/ijainn.E1071.083523
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Armoured Fighting Vehicles (AFVs) also called as Tanks play a critical role in modern warfare, providing mobility, protection and firepower on the battlefield. However, maintaining these complex machines and ensuring their operational readiness is a significant challenge for military organizations. Traditional maintenance practices are often reactive, resulting in unexpected failures, increased downtime, and operational inefficiencies. This paper focuses on the application of Artificial Intelligence (AI) for predictive maintenance of Armoured Fighting Vehicles. By harnessing the power of AI algorithms and advanced data analytics, predictive maintenance aims to anticipate and address potential equipment failures before they occur. This proactive approach enables military organizations to optimize resource allocation, improve operational planning and extend the lifespan of AFVs. The integration of AI in predictive maintenance involves collecting and analysing data from various sensors installed on the AFV engine. These sensors monitor key parameters, such as engine performance, temperature, vibration and fluid levels to detect anomalies and deviations from normal operating conditions. AI algorithms process this data, utilizing machine learning techniques to identify patterns, correlations, and potential failure indicators. The benefits of AI-based predictive maintenance for AFVs are multifaceted. Firstly, it enhances equipment readiness by reducing unexpected failures and maximizing operational availability. Secondly, it enables optimized resource allocation, ensuring that maintenance activities are scheduled efficiently, minimizing downtime, and improving overall operational efficiency. Thirdly, the predictive capabilities of AI help military planners in better decision-making allowing for improved mission planning and execution. However, the successful implementation of AI for predictive maintenance of AFV engine requires overcoming several challenges. These include data collection and integration from diverse sensors, ensuring data accuracy and quality, establishing robust communication infrastructure, and addressing cyber security concerns to protect sensitive vehicle data. This paper underscores the growing importance of AI in revolutionizing maintenance practices for Armoured Fighting Vehicles. By shifting from reactive maintenance to predictive strategies, military organizations can enhance their operational capabilities, reduce costs, and ensure the optimal performance and longevity of their AFV fleet.
Keywords: Armoured Fighting Vehicles, Predictive Maintenance, Artificial Intelligence, Data Analytics, Machine Learning, and Operational Readiness.
Scope of the Article: Artificial Intelligence