Conference Agenda

Various monitoring technologies have been developed for last several decades for aircraft structures. These technologies can inspect and monitor damages without human interrogation. However, if these technologies were to be applied to practical inspection and maintenance, it would be necessary to have a certification procedure. The US Air Force developed the aircraft structural integrity program (ASIP) in the 1970s. This program was established based on nondestructive inspection (NDI) technology. Due to the development of structural health monitoring (SHM) technologies, it would be necessary to assess different technologies from the perspective of ASIP. At the same time, it would be necessary to revise the original ASIP procedure to adapt to the new SHM technologies.

The aim of this paper is twofold. First, we evaluate different SHM technologies from the perspective of ASIP. This includes the range of inspection, detectible damage size, probability of detection, and installation weight. This will provide the technical readiness of different SHM technologies. Second, we revise the original ASIP guideline from the perspective of SHM technologies. This includes inspection intervals and life-cycle prognosis. Our goal is to make SHM technologies to be practical, what areas need to be improved and what guidelines need to be revised.

Composite structures strongly suffer of impact induced barely visible impacts, which require for proper structural health monitoring (SHM) system. Among the different degrees of information that an SHM system can provide, the primary information is mainly focused on impact localization. Among many SHM techniques, acoustic emission (AE) technique exploits elastic waves generated by the release of energy when impact occurs to localize the impact event. As many techniques can be developed relying on AE, reliability of the system is of paramount importance. The objective of this paper is to refine localization techniques and assess reliability
thereof for final optimization. The methodologies used are based on the improvement of the existing ∆T method by exploiting the nearest sensor logic and domain regions of the sensor pairs in order to obtain the localization probability distribution. A specific framework is defined to derive the Probability of Localization (POL) of the conceived SHM systems using AE technique, namely to assess the reliability thereof. Using a 15mm grid to train the AE system, the results obtained on the flat plate can reduce the error in localization to 21.74mm, with an improvement of approximately 50% compared to classic approach.

Structural health monitoring systems are known to bring numerous advantages from early detection of defects to increasing lifetime of structures. There is a need for quantified reliability estimation to enable the usage of SHM systems in critical industries, such as aerospace. Although there are established reliability estimation tools such as probability of detection, their application to SHM systems remains challenging due to the high costs in manufacturing and time. The model assisted probability of detection (MAPOD) offers a unique solution, where the structures and SHM systems are represented in virtual environment, significantly reducing the associated costs. It is however necessary to take into account all parameters relevant to the monitoring scenario, especially the environmental and operational conditions, as well as generating numerical data with the same statistical dependencies as in experimental data. In this work, we applied the MAPOD methodology on the guided wave monitoring of growing delamination in a composite shear panel containing four stiffeners. Several influencing parameters were considered, namely sensor bonding and positioning, elastic properties of composite lay-ups as well as temperature and, finally, defect characteristics. Data were generated using the CIVA SHM module. At first, we generated several monitored structures, by fixing material and sensor properties, then we considered growing defects with varying temperatures. The simulation results are then processed following different scenarios to finally obtain a POD: first, 2D images are generated using either RAPID (reconstruction algorithm for probabilistic inspection of defect) or IB (instantaneous baseline). From resulting images, two different damage indices, namely (1) maximum amplitude and (2) area under receiver operating characteristics curve, are used to estimate MAPOD curves. MAPOD curves are finally generated using two different algorithms: linear mixed methods (LMM) and size of defect at detection (also known as length at detection - LaD). The effect of the choices made in the data processing will be discussed in the talk, showing the potential of MAPOD to qualify a complete methodology.

Acknowledgement: This project has received funding from the Clean Aviation Joint Undertaking under grant agreement No. 101102010, project ‘Hybrid Electric Regional Wing Integration Novel Green Technologies’ - HERWINGT.

Commercial Aviation operators often perform scheduled maintenance, including Nondestructive Inspection (NDI), at predetermined intervals that typically do not match regular scheduled checks. This misalignment can require the aircraft to be taken out of service at inconvenient and costly times. Condition Based Maintenance (CBM) can allow operators to optimize maintenance by scheduling these inspections during the times when the aircraft is planned to be down for scheduled checks. FAA research at the William J Hughes Technical Center for Advanced Aerospace is investigating the capability of SHM technologies to support data-driven maintenance decisions integral to performing CBM. The FAA has partnered with SHM OEMs, Anodyne Electronics Manufacturing Corp (AEM) – Comparative Vacuum Monitoring (CVM) system and Metis Design Corporation – Carbon Nanotube (CNT) sensor to fatigue test 5 panels with controlled crack growth. The objective of this research is to correlate SHM sensor output data with measured crack growth data to develop a reliable, color-coded indication system that reflects the progression of flaw growth to operators. The system aims to demonstrate how SHM data could be used to characterize flaw growth in a way that allows maintenance actions to be anticipated and scheduled more effectively, ultimately enhancing aircraft safety, availability, and the cost-effectiveness of maintenance programs.

Structural design in practice still relies largely on code-based traffic load models that are calibrated at network level and cannot reflect the actual loading of individual bridges. Previous work by the authors has shown how measurement data from structural health monitoring (SHM) can be used to update the bending stiffness of an individual structure and then revise the partial safety factor on the action side, assuming a known, deterministic load. In this paper, this framework is extended to the practically relevant case of stochastic traffic loads and to the identification of monitoring-based stress and stress range processes. A joint probabilistic model for structural parameters, stochastic traffic loads, and the resulting stress responses at fatigue-critical bridge details is formulated. The traffic load is described by a stochastic model for axle loads and distances, with unknown distribution parameters that represent the traffic actually observed at the site. The forward model links these uncertain parameters to measured quantities using influence lines or a state space representation of the structural dynamics, enabling the use of SHM data for inverse identification. In a first step, a discrete state space model and a Kalman filter are used to reconstruct node displacements from a limited number of acceleration measurements. For a simply supported beam with a small number of accelerometers, the Kalman filter provides displacement time histories at all nodes and thus a physically consistent estimate of the quasi-static and low-frequency response. In a second step, these reconstructed displacements (or equivalent strains derived from them) are treated as measurements in a Bayesian load identification process. Displacement and/or strain time histories at selected sections are used to derive axle loads for individual vehicle passages, taking into account the assumed traffic kinetics (speed and axle configuration). The resulting joint posterior distribution defines time-dependent posterior prediction distributions of stresses and stress ranges at fatigue-critical locations specific to the monitored bridge and its actual traffic. The article focuses on the formulation and numerical implementation of the combined Kalman filter and Bayesian load update scheme, as well as on presenting the effects of monitoring on the uncertainty of stress effects compared to conventional code-based traffic models. The stress and stress range distributions obtained through monitoring provide a consistent basis for subsequent fatigue reliability assessment and design and form an important building block for future sensor-based calibration of partial safety factors for fatigue limit states.

Structural integrity monitoring of aluminium components in marine environments requires high-sensitivity detection methods to identify material degradation before catastrophic failure. This study presents an experimental investigation into the corrosion monitoring of an aluminum block submerged in a 3.5% NaCl solution over a 39-day duration. Utilizing a centrally bonded Lead Zirconate Titanate (PZT) patch, the Electro-Mechanical Impedance (EMI) technique was employed to capture high-frequency conductance and susceptance signatures at 3-day intervals using a C60 Impedance Analyzer. The experimental results demonstrate a clear correlation between exposure time and signature evolution; specifically, a consistent leftward shift in resonant frequencies and a progressive increase in conductance peak magnitudes were observed as corrosion damage intensified. To quantify these changes, a comprehensive statistical framework was implemented. Root Mean Square Deviation (RMSD) heatmaps were generated to track the magnitude of damage progression, while Pearson’s Correlation Index heatmaps were utilized to evaluate the loss of signature similarity across the 39-day spectrum. Additionally, Frequency Domain Integration (FDI) and Discrete Wavelet Transform (DWT) were also utilized to check the efficiency of the corrosion detection with respect to time. The integration of these statistical indices successfully characterized the corrosion stages, proving that the EMI technique, coupled with similarity based heatmaps, provides a robust and non-destructive approach for the early detection of saline induced structural damage in aluminum alloys.