Aircraft Structural Health Monitoring Using Machine Learning

Structural Health Monitoring (SHM) plays a critical role in ensuring the safety and performance of modern aircraft. This review presents a data-driven approach for detecting structural anomalies based on sensor data such as strain, vibration, and temperature. A Random Forest Classifier is employed to differentiate between healthy and damaged states of aircraft structures. By simulating sensor data and applying machine learning techniques, the proposed method demonstrates high accuracy in classifying structural conditions. Visualization techniques such as pair plots and feature importance graphs further enhance the interpretability of the model. This paper also discusses the relevance of SHM in predictive maintenance and provides insights into the use of Python-based libraries including NumPy, pandas, scikit-learn, seaborn, and matplotlib.