We present a generic methodology for developing a Health Indicator out of strain-based Structural Health Monitoring data suitable for implementation in prognostic tasks. For this purpose, an in-house test campaign is launched. Single-stringered composite panels are subjected to compression-compression fatigue with the strains being monitored with Fiber Bragg Grating sensors located along the stringers’ feet. Three different fatigue scenarios with increased complexity are investigated i.e. constant amplitude fatigue, variable amplitude fatigue and finally random amplitude (spectrum) fatigue. In this paper, we propose a fusion scheme based on Genetic Algorithms, with the resulted fused Health Indicator achieving high monotonicity and prognosability, both crucial attributes for an enhanced performance of prognostic algorithms. Finally, a popular machine learning algorithm, i.e. Gaussian Process Regression, is employed in order to predict the Remaining Useful Life of the panels in the test set. It is evidenced that the newly proposed fused Health Indicator predicts the Remaining Useful Life far more accurately as several popular performance metrics indicate. The methodology retains a data agnostic character able to be applied in Structural Health Monitoring data from different sensing technologies.

A digital twin representative of a typical composite stiffened panel is utilized to monitor skin-to-stringer disbonds. A validated finite element model of the composite panel estimates the longitudinal strains of the pristine state, at the exact location where integrated fiber Bragg grating sensors are permanently installed. Experimental strains are acquired and compared to those provided by the digital twin in order to reveal the presence of disbonds. The integrated sensor grid is used in a manner that some sensors identify the load acting on the panel, leveraging on the digital twin baseline, whilst the remaining ones are dedicated for diagnostic purposes. Two damaged single-stringer panels are tested under compression-compression fatigue conditions. Static strains are received during quasi-static test intervals among the fatigue cycles. The historical strain data are analyzed in a near real-time manner to detect and localize the induced damage throughout the test span.

Prognosis of the Remaining Useful Life (RUL) of a structure from Structural Health Monitoring data is the ultimate level in the SHM hierarchy. Reliable prognostics are key to a Condition Based Maintenance paradigm for aerospace systems and structures. In the present work, we propose a methodology for RUL prognosis of generic aeronautical elements i.e. single stringered composite panels subjected to compression/compression fatigue. Strain measurements are utilized in this direction via FBG sensors bonded to the stiffener feet. The strain data collected during the fatigue life are processed and used for the RUL prognosis. In order to accomplish this task, it is essential to produce Health Indicators (HIs) out of raw strain that can properly capture the degradation process. To create such HIs a new pre/post-processing technique is employed and a variety of different HIs are developed. The quality of the HIs can enhance the performance of the prognostic algorithms, hence a fusion methodology is proposed using genetic algorithms. The resulted fused HI is used for the RUL estimation of the SSCPs. Gaussian processes and Hidden Semi Markov Models are employed for RUL prognosis and their performance is compared. Despite the complexity the raw data we demonstrate the feasibility of successful RUL prognostics in a SHM-data driven approach.

In this paper we execute a complex test campaign to develop a novel methodology for the Remaining Useful Life (RUL) estimation of complex multi-stiffened composite aeronautical panels utilizing Machine Learning models trained with Structural Health Monitoring (SHM) data from hierarchically simpler elements, i.e., single-stiffened panels. Distributed Fiber Optical sensors (DFOS) are employed to monitor the panels’ behavior undergoing variable amplitude compression-compression fatigue after multiple impacts. A data processing methodology is first applied to the DFOS data, to both alleviate the effect of the variable loading conditions on the monitored strain and ease the computational burden. In this upscaling endeavor, an advanced strain-based Health Indicator (HI) based on Genetic algorithms, created and validated on the single-stiffened panel data, is utilized as the prognostic feature for the RUL estimations of the multi-stiffened panels. The HI displays favorable characteristics in terms of monotonicity and prognosability which are highly desirable for more accurate RUL estimations. For the prognostic task, standard machine learning models are trained using the historical degradation data of the single-stiffened panels and a similarity analysis is performed to enhance the accuracy when predicting the RUL of the multi-stiffened panels. Despite the increased structural complexity of the multi-stiffened panels, we demonstrate that the RUL is able to be predicted with reasonable accuracy. The present work paves the road for upscaling and applying prognostic methodologies to more complex structures beyond simple coupons or generic elements.

An increasing interest for Structural Health Monitoring has emerged in the last decades. Acoustic emission (AE) is one of the most popular and widely studied methodologies employed for monitoring, due to its capabilities of detecting, locating and capturing the evolution of damage. Most literature so far, has employed AE for characterizing damage mechanisms and monitoring propagation, while only a few employ it for real time monitoring and even fewer for Remaining Useful Life (RUL) prognosis. In the present work, we demonstrate a methodology for leveraging AE recordings for prognostics of composite aerospace structures. Single stiffened CFRP panels are subjected to a variety of compressive fatigue loadings, while AE sensors monitor the panels’ degradation in real time. Several AE features, both from the time and frequency domains, are utilized to identify features capable of capturing the degradation and used as Health Indicators for RUL prognosis. The choice of Health Indicators is predominantly made based on three prognostic attributes, i.e. monotonicity, trend and prognosability, which can overall affect the prognostic performance. RUL prediction of the panels is performed by employing two prominent machine learning algorithms, i.e. Gaussian Process Regression and Artificial Neural Networks. It is evidenced that the proposed AE-based methodology is highly capable to be utilized for RUL prediction of composite structures under variable loading conditions.

In this study, a multi-level Structural Health Monitoring methodology for stiffened composite panels is introduced. A digital twin (DT), that is, a three-dimensional finite element (FE) model, representing the pristine state baseline of the test article, is developed and verified for compressive loading in the post-buckling regime. The detailed FE model is utilized to train a surrogate model with respect to exogenous input, that is, axial load magnitude. The surrogate assists the DT concept that would allow prediction of the load acting on the structure based on an influx of strain data, acquired from fiber Bragg grating sensors permanently attached along the stringer feet. For this purpose, we leverage on the observation that remote from the damage, the strain field remains virtually unaltered with regard to the pristine state. The load is estimated by a sensor placed far from the damage whilst the diagnostic actions are performed by exploiting measurements from the remaining sensing locations. A health indicator, which compares the experimentally received strains with those from the surrogate representing the pristine state, is utilized to (1) detect, (2) localize, and (3) characterize the damage. As damage, we consider either skin-to-stringer disbond or initial impact damage propagation as well as overall stiffness degradation during thousands or millions of fatigue cycles. The sensors that have detected a disbond are dedicated to evaluating the potential propagation of it, while the remaining sensors evaluate the overall stiffness degradation. The proposed methodology is tested for one artificially disbonded and two impacted single-stringer panels subjected to block loading compression-compression fatigue.

In order to reduce aircraft downtimes Condition-Based-Maintenance (CBM) is a topic gaining increased popularity in recent years. However, to apply such maintenance policies reliable health monitoring techniques should be implemented. Two state of the art monitoring techniques, namely Fiber Bragg Gratings (FBG) and Acoustic Emission (AE) are used to monitor the fatigue behavior of single stiffened composite panels (SSCPs) subjected to variable amplitude compression-compression (C-C) fatigue. Advanced features, called Health indicators (HIs) are extracted from the raw sensor data to monitor the degradation behavior. It is crucial to have robust and reliable HIs that capture the degradation of the structures. This work focuses on providing capable HIs for monitoring degradation of composite structures.

The development of health indicators (HI) of diagnostic and prognostic potential from generally uninformative raw sensor data is both a challenge and an essential feature for data‐driven diagnostics and prognostics of composite structures. In this study, new damage‐sensitive features, developed from strains acquired with Fiber Bragg Grating (FBG) and acoustic emission (AE) data, were investigated for their suitability as HIs. Two original fatigue test campaigns (constant and variable amplitude) were conducted on single‐stringer composite panels using appropriate sensors. After an initial damage introduction in the form of either impact damage or artificial disbond, the panels were subjected to constant and variable amplitude compression–compression fatigue tests. Strain sensing using FBGs and AE was employed to monitor the damage growth, which was further verified by phased array ultrasound. Several FBGs were incorporated in special SMARTapes™, which were bonded along the stiffener’s feet to measure the strain field, whereas the AE sensors were strategically placed on the panels’ skin to record the acoustic emission activity. HIs were developed from FBG and AE raw data with promising behaviors for health monitoring of composite structures during service. A correlation with actual damage was attempted by leveraging the measurements from a phased array camera at several time instances throughout the experiments. The developed HIs displayed highly monotonic behaviors while damage accumulated on the composite panel, with moderate prognosability.

Real-time Structural Health Monitoring (SHM) of aeronautical structural components is a technology persistently investigated the last years by researchers and engineers to potentially reduce the cost and/or implementation of scheduled maintenance tasks. To this end, various types of sensors have been proposed to serve this role, e.g. piezoelectric, acoustic emission, and strain sensors. In the present paper, a strain-based SHM methodology is proposed for skin/stringer disbond propagation health monitoring. Fiber-optic strain sensors with engraved Bragg gratings are utilized in order to evaluate the propagation of artificially-induced disbonds at single-stringered composite panels. The specimens are subjected to a block loading compression-compression fatigue spectrum. Longitudinal static strains are periodically acquired during quasi-static loadings every 500 cycles. A Health Indicator (HI), based on strains received from the stringer’s feet, is proposed and utilized to monitor the disbond growth. The evolution of this indicator is experimentally monitored throughout the lifespan of the specimens. The present paper verifies and consolidates via actual fatigue experiments the potential of the proposed static-strain based HI developed from numerical data in our previous work.