Despite several existing techniques for distributed sensing (temperature and strain) using standard Single-Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most decoupling techniques require special optical fibers and are difficult to implement with high-spatial-resolution distributed techniques, such as OFDR. Therefore, this work’s objective is to study the feasibility of decoupling temperature and strain out of the readouts of a phase and polarization analyzer OFDR ($\varphi$-PA-OFDR) taken over an SMF. For this purpose, the readouts will be subjected to a study using several machine learning algorithms, among them Deep Neural Networks. The motivation that underlies this target is the current blockage in the widespread use of Fiber Optic Sensors in situations where both strain and temperature change, due to the coupled dependence of currently developed sensing methods. Instead of using other types of sensors or even other interrogation methods, the objective of this work is to analyze the available information in order to develop a sensing method capable of providing information about strain and temperature simultaneously.

Despite existing several techniques for distributed sensing (temperature and strain) using standard Single Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most of the decoupling techniques require special optical fibers and are difficult to implement with high spatial resolution distributed techniques, such as ϕ-PA-OFDR. So, this work’s objective is to study the feasibility of decoupling temperature and strain out of a ϕ-PA-OFDR readouts taken over an SMF. For this purpose, the readouts will be subjected to a study using several Machine Learning algorithms, among them, Deep Neural Networks. The motivation which underlies this target is the current blockage in the widespread use of Fiber Optic Sensors in situations where both strain and temperature change, due to the coupled dependence of currently developed sensing methods. Instead of using other types of sensors or even other interrogation methods, the objective of this work is to analyze the available information in order to develop a sensing method capable of providing information about strain and temperature simultaneously.

Although a crack creates a significant strain field at its tip, its effect on the strain field becomes nearly negligible only a few centimeters away from the crack, which complicates the task of damage detection. Two approaches are currently in use. The first one is a local approach that can detect damage if it intersects the optical fiber path; it is straightforward to implement but is limited to cases where the potential damage location can be anticipated (for example, in a concrete beam under flexural loads or around aircraft cargo doors). The second one, a global approach, seeks to identify damage anywhere in the structure by detecting subtle changes in the field of global strain. There is a need for algorithms to compare the strain dataset before and after damage. Machine learning offers tools to achieve this, but these tools have to be carefully selected to achieve good damage detectability. In this paper, we compare algorithms based on multivariate data analysis as well as data processing using neural networks, comparing their performance on a real structure.

This research looks at the application of Deep Neural Networks (DNNs) for low-energy impact localization in composite structures, a key aspect of structural health monitoring in the aerospace sector. The methodology used in this study involves the generation of a consistent impact dataset using an autonomous impact machine, followed by meticulous data processing. The training of the DNN models was focused on minimizing the Euclidean distance between the predicted and actual impact positions employing custom loss functions. This study yielded several significant findings. First, it confirmed the feasibility of using DNNs for effective impact localization in complex composite structures, although with varying degrees of accuracy across different impact locations but with an average error of the same order as the labeling error. Second, it was observed that the performance of the models was considerably influenced by structural features, such as the presence of stringers and the placement of sensors. The architecture demonstrated consistent performance across multiple trained models, indicating their robustness and potential for generalization. The implications of these findings for structural health monitoring are substantial, suggesting that DNNs can be a valuable tool for early damage detection in composite structures.

Due to the nature of composites, the ability to accurately locate low-energy impacts on structures is crucial for Structural Health Monitoring (SHM) in the aerospace sector. For this purpose, several techniques have been developed in the past, and, among them, Artificial Intelligence (AI) has demonstrated promising results with high performance. The non-linear behavior of AI-based solutions has made them able to withstand scenarios where complex structures and different impact configurations have been introduced, making accurate location predictions. However, the black-box nature of AI poses a challenge in the aerospace field, where reliability, trustworthiness, and validation capability are paramount. To overcome this problem, Explainable Artificial Intelligence (XAI) techniques emerge as a solution, enhancing model transparency, trust, and validation. This research presents a case study: a previously trained Impact-Locator-AI model is, initially, demonstrating a promising location accuracy; however, its behavior in real-life scenarios is unknown, and before embedding it in an aerospace structure as an SHM system its reliability must be tested. By applying XAI methodologies, the Impact-Locator-AI model can be critically evaluated to assess its reliability and potential suitability for aerospace applications, while also laying the groundwork for future research at the intersection of XAI and impact location in SHM.