A Sensor Placement Approach to Monitor Vibrations Based on Data-Driven Principal Component Analysis Techniques

Abstract This paper presents a data-driven approach for optimizing sensor placement in vibration testing of aerospace structures using Principal Component Analysis (PCA). The study aims to address inefficiencies in traditional methods of sensor placement by leveraging PCA’s capability to identify critical features in high-dimensional datasets. The methodology integrates finite element analysis (FEA) for data generation, MATLAB for data processing, and PCA for sensor optimization. Initial validation on a cantilever beam structure highlights the method’s ability to select optimal sensor locations efficiently. Subsequently, the approach is applied to a complex Alouette helicopter blade, demonstrating scalability and effectiveness. PCA results show that strategically placed sensors capture the most significant vibrational responses, reducing redundancy and enhancing the quality of vibration data. This methodology provides a cost-effective solution to enhance modal analysis and structural health monitoring in aerospace applications.