Guided wave testing (GWT) is widely employed nowadays in the structural health monitoring (SHM) of plate-like and tubular components, offering long-range inspection capabilities and sensitivity to local geometric and material discontinuities. However, practical use remains challenged by the complexity of GW signals: multimodal propagation, dispersion, and multiple reflections from boundaries or attachments often mask the influence of small or closely spaced defects. Robust signal-decomposition tools are therefore essential to isolate relevant wave packets and extract interpretable features for reliable diagnostics.

This work proposes a physics-informed matching pursuit (MP) framework [1,2] for damage detection in pipes. MP decomposes a measured waveform into a sparse expansion of propagated basis atoms—here referred to as initial wave packets (IWPs)—enabling the separation of overlapping components and filtering of noise. The incorporation of dispersion curves and mode shapes computed via the semi-analytical finite element (SAFE) method improves interpretability, enforces physical realism, and mitigates the selection of spurious, non-physical atoms [3].

The methodology is validated on a reference experimental setup consisting of a pipe instrumented with piezoelectric transducers. Signals are collected under pristine and damaged conditions, with defect size and number progressively increased to induce measurable perturbations in the received waveforms. SAFE simulations are used to construct the physics-informed model, after which MP is applied to extract wave packets and quantify variations in atom coefficients associated with damage. The approach also enables rapid switching between IWPs, as most information is retained across input cases, supporting efficient analysis.

Results show that the proposed framework enhances defect detectability while maintaining high interpretability. Embedding modal information guides MP toward atoms that match expected guided-wave characteristics, resulting in cleaner decompositions, lower residual energy, and improved separation of overlapping reflections. The sparse representations make defect-induced changes more evident, supporting earlier and more reliable identification of damage signatures.

Overall, this study demonstrates that incorporating wave physical constraints into sparse-decomposition algorithms provides a powerful means for fast, accurate, and interpretable processing of guided-wave signals in pipe inspection. The framework is general and can be extended to other geometries, sparse-representation techniques, and incorporated into machine-learning-based automated SHM methods.

References

[1] S. G. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries”, IEEE Transactions on Signal Processing, vol. 41, pp. 3397–3415, 1993.

[2] S. Rodriguez, M. Rébillat, S. Paunikar, P. Margerit, E. Monteiro, F. Chinesta, and N. Mechbal, “Single atom convolutional matching pursuit: Theoretical framework and application to Lamb waves based structural health monitoring”, Signal Processing, vol. 231, pp. 1650–1684, 2025.

[3] J. Rostami, P.W.T Tse, and Z. Fang, "Sparse and Dispersion-Based Matching Pursuit for Minimizing the Dispersion Effect Occurring when Using Guided Wave for Pipe Inspection", Materials, vol. 10, pp. 622, 2017.

Composite materials are increasingly adopted in aerospace applications due to their high strength-to-weight ratio, which enables significant weight reduction, improved fuel efficiency, and lower CO₂ emissions. Ensuring the structural integrity of such materials is therefore critical, particularly in flight-critical components such as aircraft wings equipped with Ice Protection Systems (IPS). Within the framework of the EU project PLEIADES, this research presents an efficient and unified methodology for damage detection, identification, and prognostics of composite aerospace structures using vibrational signals from integrated piezoelectric (PIC) sensors.
The proposed approach employs an integrated framework comprising a 1D Timoshenko beam-based [1] electro-thermo-mechanical (ETM) analytical model and a multiparametric Unified Butterfly Optimisation Algorithm (UBOA) to characterise bulk and delamination damages in 3D finite element (FE) beams. The analytical ETM model simulates the vibratory response of electrothermal ice protection laminate comprising heaters sandwiched between fibreglass layers and vitrimer polymer with embedded sensors. The vibratory signal from the ETM model is then optimised by multiparametric UBOA technique which combines local exploitative and global explorative search strategies to synchronise damage detection, localisation and quantification.
In the prognostic phase, the analytical model is extended to assess the influence of damage on the static, dynamic and potentially thermo-mechanical response of the degraded beam. Validation against 3D FE simulations demonstrates that the proposed method achieves substantial computational savings while maintaining a high level of accuracy as shown in Figure 1. This unified framework therefore offers a promising basis for real-time Structural Health Monitoring (SHM) and damage prognostics of composite aircraft wing structures, supporting the goals of PLEIADES toward enhanced safety and reduced maintenance costs through smart sensing and integrated SHM-IPS systems. The above research is supported by project PLEIADES (Advancing Aerospace Composites through Induction Welding & New Vitrimeric Formulations Enhanced by Integrated Photonic Sensors, Providing Data to Digital Supply Chain, SHM, Maintenance) - European Union’s Horizon Europe research and innovation program under grant agreement no. 101192721.

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Structural Health Monitoring (SHM) is crucial for detecting and assessing damage in engineering structures made of composite materials, which are widely used in critical applications such as aircraft and exhibit complex failure modes. The need to reduce maintenance costs, along with the demand for techniques capable of identifying imperceptible damage, highlights the importance of developing advanced SHM approaches. The objective of this work is to refine and advance a patented active structural health monitoring approach, enabling the detection and characterization of imperceptible damage in composite materials through high-frequency strain patterns. The technique combines piezoelectric transducers (PZTs) to excite the structural component and fiber Bragg grating (FBG) sensors to locally measure the resulting strain fields. The sensor signals are filtered to isolate the response caused by the excitation, removing contributions from temperature variations as well as from primary and secondary loads. The resulting strain maps are then analyzed using an artificial intelligence algorithm for damage detection, enabling accurate characterization of imperceptible defects. Preliminary evaluations suggest that the integration of FBG, PZT, and neural network analysis can detect and monitor subtle structural changes in composite materials. The technique is expected to enhance early-stage damage detection, provide insights into damage progression, and support predictive maintenance strategies when compared to conventional methods. This study demonstrates that the combination of advanced sensing technologies with neural networks represents a promising step toward more intelligent, adaptive, and cost-effective SHM systems. The approach has the potential to improve the reliability and efficiency of composite structures and provides a solid foundation for future research and practical implementation in real-world applications.

This paper presents the Physics-Informed Impact Identification (Phy-ID) framework. It addresses the challenges of reconstructing impact parameters from passive sensor signals. Phy-ID integrates physical knowledge into machine learning across three complementary levels: how data representation is defined, how the model is built, and how optimisation is guided. Each level is described conceptually and illustrated with documented examples from previous work of the authors and the literature. Examples cover both established strategies and new directions yet to be applied to impact identification. By aligning model design with prior knowledge of composite structures under impact, Phy-ID provides a structured, scalable modelling approach. It targets improved robustness, interpretability, and generalisation in conditions of partial physical knowledge and limited experimental data, which are typical in real-world SHM.

Digital twins are emerging as powerful tools for monitoring, simulating and optimizing physical systems in real time. However, their development in gas leak identification and localization is hindered by the scarcity of sensor data as well as the overwhelming cost of the high fidelity physical model. The present work aims to develop a novel approach leveraging synthetic data generation and a simplified physical model to enable the gas leak diagnosis on the leakage state and its localization. Synthetic data are created using a combination of Gaussian noise and existing sensor data as well as simulation data under various leak scenario and environmental conditions. These datasets are used with physical model to identify the leakage state and its localization with high accuracy. Meanwhile, the simplified physical model is based on a one-dimensional flow model under the assumption of reduced dynamic factor with low computational overhead, enabling fast updates, and adaptive forecasting. The integration of synthetic data and physical modeling offers a practical and efficient approach for deploying digital twins where real data are limited or expensive to collect. The proposed approach is applied on a French power-to-gas installation called ``Jupiter 1000'', established in 2020 and operated by NaTran. Its application on the described system focuses on leak scenario generation, leak diagnosis and leak localization. The validations of the approach are, furthermore, carried out based on risk scenarios likely to occur on the installation.