Quantifying the Influence of Artificial Intelligence and Machine Learning in Predictive Maintenance for Vehicle Fleets and Its Impact on Reliability and Cost Savings - Eswararaj, Dinesh; Koppada, Lakshmana Rao; Bodala, Ram Sekhar (International Journal of Computer Sciences and Engineering, 2025)
AI and ML are redefining predictive maintenance for vehicle fleets to boost uptime and cut expenses. We evaluate key academic and industry examples of predictive maintenance's uses, challenges, and potential advances. Start with traditional maintenance problems and AI/ML disruption. Next, we'll discuss predictive maintenance's history, goals, issues with conventional methods, and the shift to proactive measures. Studies on bus fleets, oil and gas operations, and engine reliability show AI-driven predictive maintenance's effectiveness. In view of AI's importance in fleet management, the essay examines data collection, preprocessing, and predictive maintenance ML algorithms. Case studies and real-world implementations demonstrate these technologies' successes, failures, and lessons. AI and quantum breakthroughs in electric cars and hidden patterns in heavy vehicle maintenance data create a holistic view of various sectors' uses and issues. Analysis of how proactive maintenance scheduling, condition-based monitoring, and predictive analytics improve dependability and downtime. A bus fleet and oil and gas production study found that AI-driven solutions improve fleet reliability. AI-driven predictive maintenance's ROI and financial benefits show its value. Case studies on automobile engine dependability and AI cost implications demonstrate these technologies' advanced financial benefits. Future predictive maintenance technologies and trends are discussed last. It highlights how edge, IoT, 5G, digital twins, and quantum computing may improve preventative maintenance. Strategic planning, cybersecurity, and workforce skill development are prioritized, but the changing landscape brings challenges and opportunities. This study extensively examines AI and ML-based car fleet predictive maintenance. It acknowledges predictive maintenance's future potential and limitations. It shows how these technologies may transform reliability, downtime, and costs using data from several enterprises.