Multi-Sensor Fusion for Aerospace Structural Health Indicators: Passive and Active Sensory Networks

Abstract

Developing reliable health indicators (HIs) for aeronautical composite structures is challenging because damage evolution is complex, stochastic, and affected by uncertainties such as impact events and manufactured defects. In this context, passive and active structural health monitoring (SHM) techniques provide complementary information: acoustic emission (AE) captures temporally dense signatures of damage activity, whereas guided waves (GW) provide state-sensitive interrogation of the structure at discrete inspection times. This work therefore investigates heterogeneous sensor fusion for HI construction in composite T-stiffener panels subjected to run-to-failure compression-compression fatigue loading. Modality-specific AE- and GW-based HI frameworks are first developed independently using signal processing and AI. The resulting HIs are then synchronized in the fatigue-cycle domain and combined through inter-modality fusion to obtain a single fused HI. The study addresses key challenges including the absence of true HI labels, synchronization of passive and active measurements, and learning from limited multimodal overlap. The results show the potential of combining AE and GW to construct more informative and robust HIs for prognostics of composite aerostructures.