Multi-source sensor data fusion framework for structural health monitoring of polymer-matrix composites (PMC) based on latent-space clustering using a convolutional autoencoder

This study presents an integrated framework combining the data fusion and clustering techniques with Neural Networks (NNs) for the processing and classification of heterogeneous experimental data. The proposed approach establishes a synergistic link between materials science and data science, enabling enhanced interpretation of the complex multi-sensor datasets. A convolutional autoencoder was employed to cluster many data acquired from the load–unload tensile tests conducted on smart polymer-matrix composite (PMC) specimens embedding two Lead Zirconate Titanate (PZT) and one Polyvinylidene Fluoride (PVDF) transducers. The PZT sensors provided high-sensitivity electrical data reflecting the internal mechanical responses, while the PVDF transducer recorded complementary strain-related variations—together forming a robust dataset for Structural Health Monitoring (SHM). These internal measurements were complemented by external tools, including Digital Image Correlation (DIC) and Acoustic Emission (AE) systems, which supported both validation and benchmarking of the neural framework. The findings demonstrate that the proposed architecture effectively integrates and classifies diverse sensor signals, underscoring the necessity of clear, sensor-specific data structuring for reliable SHM applications.