This midterm research paper examines machine learning as an embedded engineering tool within modern aviation systems rather than as an isolated software discipline. Focusing on microcontroller-based architectures, the paper analyzes how sensors, embedded processors, and machine learning models work together to support safety-critical functions such as system health monitoring, prognostics, and operational decision support. Using FAA regulatory documents, academic literature on SHM/PHM, and a small-scale RC Airbus A318 avionics platform as a practical reference, the paper situates machine learning within established aviation system design practices. Emphasis is placed on validation, explainability, and safety constraints that govern the deployment of ML in real aviation environments.  

This paper examines the role of machine learning as a supporting component within embedded systems rather than as an independent or autonomous decision-making mechanism. Focusing on aviation-related applications, the study explores how machine learning integrates with sensors, microcontrollers, communication buses, and display systems to enhance system monitoring, diagnostics, and operational awareness. A case study involving an RC Airbus A318 embedded avionics platform is presented to illustrate these concepts in a practical context. The paper emphasizes system integration, reliability, and interpretability, highlighting machine learning’s function as a constrained decision-support tool within safety-critical embedded architectures.