Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical Stress
M. A. Melgarejo, Alejandro Pérez, Desireé Ruiz, Abraham Casas, Fabio A. González, Vicente Alonso
Abstract
This study presents the design and validation of a numerical method based on an AI-driven ROM framework for implementing stress virtual sensing. By leveraging Reduced-Order Models (ROMs), the research aims to develop a virtual stress transducer capable of the real-time monitoring of mechanical stresses in mechanical components previously analyzed with high-resolution FEM simulations under a wide range of multiple load scenarios. The ROM is constructed through neural networks trained on Finite Element Method (FEM) outputs from multiple scenarios, resulting in a simplified yet highly accurate model that can be easily implemented digitally. The ANN model achieves a prediction error of MAEtest=(0.04±0.06) MPa for the instantaneous mechanical stress predictions, evaluated over the entire range of stress values (0 to 5.32 MPa) across the component structure. The virtual sensor is capable of producing a quasi-instantaneous, detailed full stress map of the component in just 0.13 s using the ROM, for any combination of 4-load inputs, compared to the 6 min and 31 s required by the FEM. Thus, the approach significantly reduces computational complexity while maintaining a high degree of precision, enabling efficient real-time monitoring. The proposed method's effectiveness is demonstrated through rigorous ROM validation, underscoring its potential for stress control. This precise AI-driven procedure opens new horizons for predictive maintenance strategies centered on stress cycle monitoring.