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The integration of digital twin technology with predictive maintenance methodologies marks a significant breakthrough in the automotive industry. This comprehensive study meticulously examines the symbiotic relationship between digital twins and predictive analytics, elucidating their fundamental concepts and their crucial role in real-time monitoring and forecasting of vehicle component health. Delving deep into the complexities of implementing digital twin-driven predictive maintenance systems within automotive ecosystems, the research meticulously addresses challenges such as data aggregation, model development, and decision-making processes. It emphasizes the pivotal contribution of cutting-edge machine learning algorithms and data-driven approaches in refining the accuracy and effectiveness of predictive maintenance solutions for automobiles. Through an extensive analysis of case studies and industry exemplars, the study presents compelling examples of digital twin applications in automobile maintenance models, demonstrating their practical benefits and highlighting their potential impact on vehicle reliability, safety, and operational efficiency. Furthermore, the research outlines future research directions and emerging trends in digital twin-enabled predictive maintenance, underscoring their transformative potential in optimizing maintenance procedures and enhancing the performance of modern automobile systems. In summary, this research endeavors to provide a nuanced understanding of digital twins' transformative potential, aiming to reshape maintenance practices and maximize the efficiency of contemporary automotive systems.
Mohanraj et al. (Wed,) studied this question.