Conference Agenda

Structural Health Monitoring (SHM) relies on the deployment of permanently installed sensors over long periods (e.g., several years). However, while these devices enable the monitoring of a structure, a key question remains: how can the sensors themselves be monitored over time? Existing studies addressing this issue often focus on a specific subsystem of the sensor (e.g., piezoelectric element, battery, etc.), leading to a fragmented view of sensor reliability.

Before considering self-monitoring or self-diagnostic strategies, it is necessary to provide a conceptual clarification of what constitutes a wireless SHM sensor. It is also essential to identify the main drift and failure mechanisms affecting its various features, including electronics, data acquisition, communication, and power management.

This work provides a state-of-the-art overview based on a review of the scientific literature, complemented by industrial feedback related to failure mechanisms and metrological drift. The objective is to establish the current state of knowledge, identify the approaches already explored, whether in SHM or in related fields, and highlight the remaining gaps.

The analysis reveals the major scientific and technological challenges that must be addressed to design smarter sensors able of assessing their own health status and improving the long-term reliability of SHM instrumentations.

Modern aircrafts are increasingly in need of ice protection systems (IPS) to ensure flight safety under severe weather conditions. Whether in the anti-icing or de-icing mode, IPS typically operate based on electrical heating technology to prevent the accretion of ice or to remove ice residue formed on the aircraft wings. However, the efficiency of electric IPS remains a technological challenge to be addressed to fully optimize electrical power consumption of IPS, thus boosting the energy sustainability target of the European Green Deal. This research computationally investigates a new IPS concept consisting of a beam-like IPS stack having layers of electric heaters sandwiched between fibreglass layers and thermoplastics integrating embedded sensors measuring temperature and pressure/stress (see figure 1). The new IPS laminate comes with enhanced capability to perform real-time sensing of thermal profiles whilst capturing stress signals for structural health monitoring (SHM) of the aircraft region connected to the IPS stack. In this context, a novel electro-thermo-mechanical (ETM) beam model is developed for generating vibrational signals in IPS-like laminate based on zigzag-enriched higher order beam theory and differential quadrature analytical discretisation. The ETM model is subsequently integrated with a swarm intelligence-based optimization algorithm for the purpose of damage detection processing. A 3D finite element benchmark is further developed to calibrate the performance of the ETM model as well as generating input frequencies for the SHM tool. A demonstration of the capability of the ETM-based SHM approach reveals that rapid damage detection of the IPS laminate can be achieved with satisfactory accuracy emphasising the robustness of the scheme. As a further development, the application of photonic integrated circuit sensors embedded in the IPS laminate is envisaged to potentially advance the capabilities of aircraft intelligence towards enhanced efficiency and sustainability. The above endeavours are supported by the EU project HORIZON-CL5-2024-D5-01 PLEIADES (Advancing Aerospace Composites through Induction Welding & New Vitrimeric Formulations Enhanced by Integrated Photonic Sensors, Providing Data to Digital Supply Chain, SHM, Maintenance).

Elastic topological metamaterials (ETMs) offer a powerful platform for guiding and manipulating high-frequency elastic waves with inherent robustness. Yet, their utility has been fundamentally limited by a critical constraint: existing designs only support topological edge states for a single polarization, typically the flexural mode. This exclusivity restricts their capacity to transport the elastic wave and signal carried by other modes. Furthermore, while their potential for waveguiding is recognized, the application of ETMs for practical elastic wave signal enhancement and energy harvesting remains largely unexplored.

To overcome these limitations, we introduce a multi-polarization elastic topological metamaterial. This device is engineered to concurrently convey and harvest energy from elastic waves of multiple polarizations. It sustains broadband topological edge states for both out-of-plane and in-plane modes, facilitating the robust transmission of multi-polarized elastic wave signal and energy. By strategically integrating the waveguide with a piezoelectric element, we successfully convert the guided mechanical energy into electrical power, demonstrating efficient elastic wave energy harvesting and signal boost. Crucially, the design maintains a simple architecture, making it readily scalable and integrable into the system. This advancement has potential for structural health monitoring devices capable of simultaneous elastic wave signal communication and energy scavenging. Our work thus propels the development of versatile topological metamaterials that can simultaneously manage and harness the full spectrum of elastic wave energy, unlocking significant potential for structural health monitoring applications.

This study develops an embedded impact damage identification method for composite structures using laser-induced graphene (LIG) combined with electrical impedance tomography (EIT) and deep learning. An LIG sensing area was directly fabricated on a polyimide-based flexible printed circuit (FPC) via a CO₂ laser and embedded within the interlayer region of glass fiber reinforced polymer composites. This LIG-FPC sensor serves as a highly sensitive sensing layer for capturing localized conductivity change induced by impact damage. After impact, the change in the pathways of the conductive network results in measurable change in the boundary voltages. The boundary voltage change was acquired by an EIT system and processed by a modified residual network (ResNet18) to reconstruct internal conductivity change distribution. A simulation-based dataset mapping boundary voltage changes to conductivity change distributions was used to train the network. Experimental results demonstrate that the proposed method can precisely localize damage. This approach effectively overcomes limitations associated with traditional sensor-based methods, providing a robust, fast, and non-destructive solution for in-situ structural health monitoring (SHM) of composite structures.