Smart Aircraft Monitoring Using AI-Driven Digital Twins and IoT-Based Data Acquisition

The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin (DT) technologies is reshaping modern aerospace systems by enabling intelligent, predictive, and data-driven maintenance operations.This paper presents a modular and scalable Digital Twin framework that integrates IoT-enabled real- time data acquisition with AI-driven analytics to support aircraft lifecycle management. The proposed approach combines data-driven models— such as Random Forests and deep learning—with physics-based simulations to enhance system diagnostics, anomaly detection, and remaining useful life (RUL) estimation. A layered architecture is outlined, incorporating edge computing, cloud services, and API connectivity to facilitate seamless communication between physical systems and their virtual counterparts. The paper also addresses current challenges in interoperability, model fidelity, and regulatory constraints, while offering a roadmap for implementing hybrid AI-IoT Digital Twins in real-world aerospace applications. This work aims to guide the design of next-generationaviation systems that are adaptive, autonomous, and resilient across operational contexts.