Artificial Intelligence for Implementation of Predictive Maintenance in Indian Army

Abstract

This thesis explores the compelling case for the Indian Army's adoption of AI-Based Predictive Maintenance for its extensive vehicle fleet, comprising over a lakh vehicles, including more than 10,000 Armoured Fighting Vehicles (AFVs). The research delves into the challenges posed by the scale of this fleet, resource constraints, downtime, and the limitations of traditional maintenance approaches. It elucidates the principles of predictive maintenance, underpinned by Artificial Intelligence (AI) technologies, and the value proposition it offers.

Through a comprehensive analysis, this thesis elucidates the economic and strategic implications of implementing AI-Based Predictive Maintenance. It underscores the potential benefits, such as enhanced fleet readiness, cost savings, improved safety, extended vehicle lifespan, and real-time decision support. Furthermore, it examines the implications for national security and technological advancements that result from embracing this transformative approach.

The thesis presents a proposed roadmap for implementation, advocating a phased approach, collaboration with industry partners, and an emphasis on training and skill development. Finally, it concludes with recommendations, urging the Indian Army to adopt AI-Based Predictive Maintenance, establish a policy and regulatory framework, and invest in ongoing research and development efforts. The adoption of AI-Based Predictive Maintenance offers the Indian Army an opportunity to optimize its vehicle fleet management, enhance readiness, and contribute to national security and technological progress.