Optical fiber sensors (OFSs) represent an efficient sensing solution in various structural health monitoring (SHM) applications. However, a well-defined methodology is still missing to quantify their damage detection performance, preventing their certification and full deployment in SHM. In a recent study, the authors proposed an experimental methodology to qualify distributed OFSs using the concept of probability of detection (POD). Nevertheless, POD curves require considerable testing, which is often not feasible. This study takes a step forward, presenting a model-assisted POD (MAPOD) approach for the first time applied to distributed OFSs (DOFSs). The new MAPOD framework applied to DOFSs is validated through previous experimental results, considering the mode I delamination monitoring of a double-cantilever beam (DCB) specimen under quasi-static loading conditions. The results show how strain transfer, loading conditions, human factors, interrogator resolution, and noise can alter the damage detection capabilities of DOFSs. This MAPOD approach represents a tool to study the effects of varying environmental and operational conditions on SHM systems based on DOFSs and for the design optimization of the monitoring system.@en

Distributed Optical Fiber Sensors (DOFS) show several inherent benefits with respect to conventional strain-sensing technologies and represent a key technology for Structural Health Monitoring (SHM). Despite the solid motivation behind DOFS-based SHM systems, their implementation for real-time structural assessment is still unsatisfactory outside academia. One of the main reasons is the lack of rigorous methodologies for uncertainty quantification, which hinders the performance assessment of the monitoring system. The concept of Probability of Detection (POD) should function as the guiding light in this process, but precautions must be taken to apply this concept to SHM, as it has been originally developed for Non-Destructive Evaluation techniques. Although DOFS have been the object of numerous studies, a well-established methodology for their performance evaluation in terms of PODs is still missing. In the present work, the concept of Probability of Delamination Detection (POD²) is proposed for a DOFS network; Carbon Fiber-Reinforced Polymers (CFRP) Double-Cantilever Beam (DCB) specimens equipped with DOFS have been tested under static loading, and the strain patterns along with the relative observed delamination size have been collected to generate an adequate database for the POD analysis, suggesting a reference methodology to quantify the performance of DOFS for delamination detection.

@en

Despite the promising application of Distributed Optical Fiber Sensors (DOFS) in monitoring damage in composite structures, their implementation outside academia is still unsatisfactory due to the lack of a systematic approach to assessing their damage detection performance. The existing tool developed for non-destructive evaluation, Probability of Detection (POD) curves, needs to be adapted for structural health monitoring applications to account for spatial and temporal dependency. Damage detection performance with DOFS is deeply related to the inherent variability sources of the system, the strain transfer properties of the optical fiber, and the loading conditions, which determine the damage-induced strain on the structure. This paper establishes a systematic approach based on the Length at Detection (LaD) method to qualify DOFS for damage detection in composites under different scenarios. Specifically, this study considers two DOFS with different strain transfer properties for monitoring delamination in carbon fiber reinforced polymers double-cantilever beam specimens under mode I quasi-static and fatigue loading. The POD curves derived from the LaD method confirm that this methodology can quantify the change in the detection performance due to the DOFS type and the loading conditions. The study also proposes a practical solution to compare POD curves obtained with different sample sizes, by introducing the concept of virtual specimens to simulate the lower confidence bound convergence.

@en

Machine learning (ML) methods for the structural health monitoring (SHM) of composite structures rely on sufficient domain knowledge as they typically demand to extract damage-sensitive features from raw data before training the ML model. In practice, prior knowledge is not available in most cases. Deep learning (DL) methods, on the other hand, can obtain higher-level features from raw input data and have proven superior in several applications. This paper proposes a Convolutional Neural Network (CNN) based approach for the delamination prediction in CFRP double cantilever beam (DCB) specimens using raw local array strain measurements via distributed optical fiber sensors. The conventional CNN architecture is modified to perform regression, as the delamination size is a continuous value. 1D and 2D CNN architectures are deployed and compared and different techniques are exploited to encode 1D spatial strain pattern series as 2D images. Raw strain patterns collected during static testing are used to train the CNNs, while testing is performed on unseen raw fatigue strain patterns, showing the CNN ability to automatically extract discriminative features from the non-pre-processed static strain pattern-based signals that generalize to raw fatigue signals as well. This strategy has the potential to reduce fatigue testing expenditures while also shortening the time required to gather training data.

@en

Structural Health Monitoring (SHM) aims to shift aircraft maintenance from a time-based to a condition-based approach. Within all the SHM techniques, Acoustic Emission (AE) allows for the monitoring of large areas by analyzing Lamb waves propagating in plate like structures. In this study, the authors proposed a Time Reversal (TR) methodology with the aim of reconstructing an original and unaltered signal from an AE event. Although the TR method has been applied in Narrow-Band (NwB) signal reconstruction, it fails when a Broad-Band (BdB) signal, such as a real AE event, is present. Therefore, a novel methodology based on the use of a Frequencies Compensation Transfer Function (FCTF), which is capable of reconstructing both NwB and real BdB signals, is presented. The study was carried out experimentally using several sensor layouts and materials with two different AE sources: (i) a Numerically Built Broadband (NBB) signal, (ii) a Pencil Lead Break (PLB). The results were validated numerically using Abaqus/CAE^TM with the implementation of absorbing boundaries to minimize edge reflections.

@en

In the Structural Health Monitoring (SHM) field, Acoustic Emissions (AE) is the process by which acoustic signals generated during the formation of damage are captured by sensors, analyzed and used for localization within the structure. In plate like structures, these signals lead to the formation of Lamb Waves (LW), which are broadband in nature. These LW are generally captured by Piezoelectric Titanum Zirconate (PZT) sensors. As such, the captured broadband signals are of difficult interpretation in part due to several phenomena such as dispersion or attenuation suffered by the waves during their propagation. In this study, we hypothesize that the nature of the emitted signal contains information on the damage type, as if the features of the emitted signal were a 'fingerprint' of the damage. Wing or fuselage panels are some of the aeronautical structures were LW can develop during the emission of an acoustic signal. In operational service environments, the damage type and size may lead to the generation of different signal sources. This study aims at the development, through experimental techniques, of a classification algorithm based on Artificial Intelligence (AI) for determining the source of the emission in addition to their location within a structure. It is envisioned that the AI algorithms will be capable of identifying specific features within the emitted signals and thus correlate them to a database of known signals and their corresponding associated damage types. In order to create an AE signal damage database, the captured signal cannot be used since it has been affected by its propagation through the structure. As such, a Time Reversal process will be implemented in order to reconstruct the original signal. This original signal will be the one utilized by the AI algorithm in order to identify its corresponding damage source.

@en