This research investigates an advanced AI-based diagnostic framework to support predictive and preventive maintenance in connected vehicle environments. The study focuses on improving vehicle reliability, reducing unplanned downtime, and supporting more efficient maintenance planning by moving beyond conventional reactive or schedule-based maintenance approaches.

The proposed research explores how heterogeneous vehicle-related data, operational indicators, maintenance information, and intelligent analytical methods can be integrated to anticipate potential faults and support context-aware maintenance decisions. Positioned within a connected vehicle and Vehicle-to-Everything environment, the research aims to enable earlier risk identification, more adaptive diagnostic reasoning, and improved operational reliability across different vehicle operating conditions.

From an academic perspective, the research contributes to intelligent maintenance systems by examining the combination of data-driven learning, semantic reasoning, and causal analytical approaches for more reliable diagnostic and decision-support processes. From an industrial perspective, the research addresses the need for scalable, explainable, and implementation-oriented maintenance intelligence for automotive stakeholders, fleet operators, service providers, and technology partners.

Certain technical details, implementation mechanisms, parameterization strategies, and proprietary components are intentionally not disclosed due to ongoing research development and potential intellectual property protection.