Structural Health Monitoring Methodology Research Articles

Data-Driven Multi-Fault Detection in Pipelines Utilizing Frequency Response Function and Artificial Neural Networks

This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an artificial neural network (ANN) algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088×10−3to0.3062×10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model's effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems.

Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: Structural Health Monitoring in the Context of Industry 4.0

This paper investigates the integration of Structural Health Monitoring (SHM) within the frame of Industry 4.0 (I4.0) technologies, highlighting the potential for intelligent infrastructure management through the utilization of big data analytics, machine learning (ML), and the Internet of Things (IoT). This study presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2 mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and I4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.

Efficacy of PZT Sensors Network Different Configurations in Damage Detection of Fiber-Reinforced Concrete Prisms under Repeated Loading.

Real-time structural health monitoring (SHM) and accurate diagnosis of imminent damage are critical to ensure the structural safety of conventional reinforced concrete (RC) and fiber-reinforced concrete (FRC) structures. Implementations of a piezoelectric lead zirconate titanate (PZT) sensor network in the critical areas of structural members can identify the damage level. This study uses a recently developed PZT-enabled Electro-Mechanical Impedance (EMI)-based, real-time, wireless, and portable SHM and damage detection system in prismatic specimens subjected to flexural repeated loading plain concrete (PC) and FRC. Furthermore, this research examined the efficacy of the proposed SHM methodology for FRC cracking identification of the specimens at various loading levels with different sensor layouts. Additionally, damage quantification using values of statistical damage indices is included. For this reason, the well-known conventional static metric of the Root Mean Square Deviation (RMSD) and the Mean Absolute Percentage Deviation (MAPD) were used and compared. This paper addresses a reliable monitoring experimental methodology in FRC to diagnose damage and predict the forthcoming flexural failure at early damage stages, such as at the onset of cracking. Test results indicated that damage assessment is successfully achieved using RMSD and MAPD indices of a strategically placed network of PZT sensors. Furthermore, the Upper Control Limit (UCL) index was adopted as a threshold for further sifting the scalar damage indices. Additionally, the proposed PZT-enable SHM method for prompt damage level is first established, providing the relationship between the voltage frequency response of the 32 PZT sensors and the crack propagation of the FRC prisms due to the step-by-step increased imposed load. In conclusion, damage diagnosis through continuous monitoring of PZTs responses of FRC due to flexural loading is a quantitative, reliable, and promising application.

A preliminary investigation of the inverse Finite Element Method applied to bridge structures

Deflection monitoring plays a key role in bridge Structural Health Monitoring, as deflection data may offer valuable insights into potential structural issues, such as cracks and corrosion. However, monitoring bridge deflections under operative loads presents challenges, particularly in scenarios where establishing a fixed reference point for displacement sensors is impractical, such as when a bridge spans a waterway or the sensors may be vulnerable to vandalism. In recent years, the inverse Finite Element Method (iFEM) has emerged as a possible solution. This method provides a load- and material-independent means of computing structural displacements from strain measurements, in real time at a minimal computational cost. Additionally, the iFEM can serve as a virtual sensing approach to monitor strains at locations lacking sensors, enabling the assessment of the fatigue life consumption at virtually any point in the structure. Despite its potential, applications of the iFEM in the civil engineering field are very limited, with no validation on full scale bridges has been performed to date. This study addresses this gap by investingating the application of the iFEM for bridge deflection monitoring through distributed strain measurements. The proposed approach is tested on experimental case studies, involving cross-validatiion of reference-free deflections inferred by the iFEM with those obtained from displacement sensors. This work contributes to the advancements of structural health monitoring methodologies in civil engineering, demonstrating the practicality and reliability of iFEM in bridge applications.

УСПЕХИ В МОНИТОРИНГЕ СОСТОЯНИЯ КОНСТРУКЦИЙ: ОБЗОР ПОДХОДОВ МАШИННОГО ОБУЧЕНИЯ ДЛЯ ОБНАРУЖЕНИЯ И ОЦЕНКИ ПОВРЕЖДЕНИЙ

Structural Health Monitoring (SHM) is a crucial discipline geared towards detecting damage in engineering structures early, aiming to prevent failures and facilitate condition-based maintenance. Traditional SHM methodologies, relying on visual inspections, analytical models, and signal processing, exhibit inherent limitations. The advent of machine learning has introduced data-driven solutions to automate various aspects of SHM, including damage detection, localization, classification, and prognosis.
 This paper provides a comprehensive review of recent studies exploring supervised, unsupervised, and deep learning techniques in vibration-based, image-based, and multi-sensor SHM. Support vector machines, neural networks, deep convolutional neural networks, and other advanced algorithms have demonstrated exceptional performance in assessing damage using real-world structural datasets.
 Despite these successes, practical challenges persist, particularly in addressing variability and deploying machine learning models effectively on full-scale structures. Overcoming these challenges necessitates a more integrated, cross-disciplinary approach, merging mechanical engineering fundamentals with machine learning expertise. This synergy can pave the way for robust field implementation and further enhance the reliability of SHM systems.
 The transformative potential of machine learning in SHM cannot be understated. Beyond merely shifting from time-based maintenance to condition-based strategies, machine learning can automate and continuously evaluate structural integrity, ensuring the longevity of engineering structures. As we delve deeper into the intersection of mechanical engineering and machine learning, the prospect of a future where SHM seamlessly integrates with advanced technologies becomes increasingly tangible.

A vibration-based Machine Learning type Structural Health Monitoring methodology for populations of composite aerostructures under uncertainty

A robust to uncertainty Machine Learning (ML) based Structural Health Monitoring methodology for populations of composite aerostructures is postulated. The methodology is founded upon a number of unsupervised ML algorithms for damage detection and a supervised counterpart for damage characterization. Damage detection is specifically based on two types of Healthy Subspace representations: A Multiple Model (MM) and a varying radii Hyper-Sphere (HS) type. Both are built upon response-only vibration acceleration and/or strain signals at properly selected sensor locations. Based on them, Multiple Input Single Output (MISO) Transmittance Function AutoRegressive with eXogenous (TF-ARX) excitation data driven models representing the partial structural dynamics are obtained. Decision making is then based on the model parameter vector that may be transformed and reduced via Principal Component Analysis (PCA). Damage detection is achieved via multi-level information fusion using acceleration and/or strain sensors. Damage characterization, referring to damage type, location, and level determination, is achieved via a hierarchical cosine similarity based algorithm. The methodology is successfully assessed via hundreds of experiments using a population of small-scale composite coupons for the detection and characterization of Delamination and Impact damage under material/manufacturing, temperature, excitation, and experimental uncertainty.

Microwave Structural Health Monitoring of the Grouted Connection of a Monopile-Based Offshore Wind Turbine: Fatigue Testing Using a Scaled Laboratory Demonstrator

Offshore wind turbines play a significant role in the expansion of clean and renewable energy. However, their exposure to harsh marine environments and dynamic loading conditions poses significant challenges to their structural integrity. In particular, the grouted connection, serving as the crucial interface between the monopile and the transition piece, is susceptible to cracking and particle washout that can lead to destabilizing grout erosion over time. In this paper, we propose a microwave structural health monitoring (SHM) approach for damage detection in grouted connections based on a stepped-frequency continuous wave radar. The methodology exploits ultra-wideband (UWB) electromagnetic wave propagation in the frequency range from 100 MHz to 2 GHz, where the microwaves propagate along the concrete-type dielectric material guided by the surrounding steel cylinders. For the proof of concept, a scaled laboratory demonstrator was built that realistically models the dynamic loading experienced by a full-scale monopile. The structure was equipped with an UWB radar system using two transmitting and three receiving antennas directly coupled to the grout. For validation, a large number of other sensors, i.e., accelerometers, strain gauges, and acoustic emission sensors have also been installed and measured synchronously during the fatigue test. It is demonstrated here that the proposed SHM methodology offers a nondestructive and real-time method for assessing the structural integrity of the grouted connection directly, actively, and automatically. This has the potential to support predictive maintenance activities in the future.

Time Series Analysis Methodology for Damage Detection in Civil Structures

Structural health monitoring (SHM) methodologies employing data-driven techniques are becoming increasingly popular for detection of structural damage at the earliest stage possible. With measured vibration signals from the structure, time series modeling methods provide quantitative means for extracting such features that can be utilized for damage diagnosis. In this study, one-step prediction error of an autoregressive (AR) model over a data set is used as damage indicator. In particular, the difference between the prediction of the AR model that is fit to the measured acceleration signal obtained from the intact structure and actual measured signals collected for different damage states of the structure are interrogated for diagnosis purposes. More specifically, the standard deviation of the residual error is employed to locate the damaged region. Singular-value decomposition (SVD) is employed to find the optimal order for an AR model created using the impulse responses of the system. Numerical simulations are carried out using the impulse responses acquired from a four-story frame structure contaminated with additive noise including single and multiple damaged elements. The results of the simulations demonstrate that the method can be effectively employed to detect and locate damage. The performance of the proposed procedure are further demonstrated using the impact data acquired from a reinforced concrete frame for real applications.

Visualization of structural health monitoring information using Internet-of-Things integrated with building information modeling

Structural Health Monitoring (SHM) has become a paramount necessity in civil engineering for improving the operational performance of aging infrastructure. Recent monitoring techniques have utilized emerging technologies in Industry 4.0, such as the Internet of Things, Big Data analytics, cloud computing, and cybersecurity, to automate SHM methodologies. However, they have found challenges in linking these technologies and developing an autonomous, well-established digital framework for applications of SHM. Visualizing processed SHM data in a real-time digital interface generates multiple obstacles, such as witnessing delays in data transfer and resorting to offline tools for manual data processing. This paper, therefore, explores the integration of Building Information Modeling (BIM) and the Internet of Things (IoT) through an Arduino micro-processing unit for tracking and visualizing the data from the time and frequency domains. Strategies for enabling data monitoring and processing are developed while continuously acquiring structural responses. The query of data is established in a web-based database instead of storing the data in offline resources that await manual intervention. The proposed real-time SHM methodology is validated experimentally using two practical applications: a dynamically moving vehicle over a simply-supported bridge prototype and a randomly excited three-story model with real-time visualization of both time- and frequency-domain information under undamaged and damaged conditions. The proposed research develops an early-phase Digital Twin (DT) to present static and real-time dynamic data in a rich-fed BIM database.

Guided wave ultrasonic feature determination in Type IV composite overwrapped pressure vessels towards the digital twin

The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.

Probability based damage detection on a composite fuselage panel based on large data set of guided wave signals

This paper reports on the application and challenges of a guided wave-based structural health monitoring (GWSHM) methodology on an industrial 5 meter-long curved composite fuselage panel, for detecting barely visible impact damage (BVID), based on integrated sensors data. An extensive network consisting of 72 piezoelectric transducers installed on the panel via diagnostic film, a layer including sensors and inkjet printed flexible circuits, that provides uniform bonding quality and repairability. Threshold setting for damage detection is proposed based on statistical distribution of pristine signals, obtained throughout the span of one month. The threshold is determined through an Outlier Analysis and validated through an iterative cross-validation approach. The model validation yields a Gamma distribution with 1.5% false positive rate. A total of 20 impacts at different energies are conducted on the panel for BVID generation and these are respectively categorized based on their location. Methodologies were developed in order to detect, characterize, and localize the BVID generated from the impacts at various locations of the panel. The reliability of the SHM system is quantified by the Probability of Detection for each damage category by computing the damage area with 90% probability and 95% confidence level (a90|95 values). For damages in the skin and at the foot of the stringer a90|95 values of 233 mm2 and 365 mm2 are, respectively, obtained. Finally, the summary of the experimental work is presented through the detection of all imparted damages.

A methodology for long-term offshore structural health monitoring using stand-alone GNSS: case study in the Gulf of Mexico

This article presents a robust methodology for long-term offshore structural health monitoring (SHM) using the Global Navigation Satellite Systems (GNSS). The methodology relies on recently developed regional reference frames and single-receiver phase-ambiguity-fixed Precise Point Positioning techniques. The stable Gulf of Mexico Reference Frame 2020 (GOM20) provides a robust and consistent reference system for long-term offshore SHM in the Gulf of Mexico (GOM). Continuous GNSS observations (DEV1, 2010–2020) on a fixed platform in the Eugene Island 330 oil field are used to illustrate the methodology. The platform was installed in 1982 in 82-m water about 130 km away from the Mississippi Delta coastline. The major monitoring items include horizontal movements, seafloor subsidence, structure submergence, and seasonal oscillations. The stand-alone GNSS monitoring achieves 3- to 4-mm root-mean-square accuracy in the horizontal direction and 7 mm in the vertical direction for daily positions in the GOM region. According to this study, the GNSS antenna (DEV1) has moved 6 cm toward the northeast with respect to GOM20 since 2010; the ongoing structure submergence rate in the Eugene Island 330 oil field area is approximately 15 mm/year, a combination of seafloor subsidence (12 mm/year) and sea-level rise (2.6 mm/year) with respect to GOM20. The submergence in the future 40 years (2021–2060) would be greater than 0.6 m, likely between 0.8 and 1.0 m, but is unlikely to exceed 1.3 m. The peak-to-trough amplitudes of the seasonal movements at the top of the platform are below 5 mm in all three directions, comparable with the seasonal movements recorded by onshore GNSS in the Louisiana coastal region. The methodology introduced in this article can be applied to SHM in other offshore regions where stable regional reference frames are available.

A global-local damage localization and quantification approach in composite structures using ultrasonic guided waves and active infrared thermography

The paper emphasizes an effective quantification of hidden damage in composite structures using ultrasonic guided wave (GW) propagation-based structural health monitoring (SHM) and an artificial neural network (ANN) based active infrared thermography (IRT) analysis. In recent years, there has been increased interest in using a global-local approach for damage localization purposes. The global approach is mainly used in identifying the damage, while the local approach is quantifying. This paper presents a proof-of-study to use such a global-local approach in damage localization and quantification. The main novelties in this paper are the implementation of an improved SHM GW algorithm to localize the damages, a new pixel-based confusion matrix to quantify the size of the damage threshold, and a newly developed IRT-ANN algorithm to validate the damage quantification. From the SHM methodology, it is realized that only three sensors are sufficient to localize the damage, and an ANN- IRT imaging algorithm with only five hidden neurons in quantifying the damage. The robust SHM methods effectively identified, localized, and quantified the different damage dimensions against the non-destructive testing-IRT method in different composite structures.

Ultrasound-based structural health monitoring methodology employing active and passive techniques

Currently, structures are examined during manufacturing by means of Non Destructive Tests (NDT), but there is an increasing interest in monitoring its integrity over its whole life cycle by using Structural Health Monitoring (SHM) strategies. The monitoring of aircraft structures is particularly important as they suffer high strain under extreme atmospheric conditions. There is an extensive literature on SHM for aviation available but there are few references on comprehensive methodologies. This article introduces a methodology, a device and the tests used in its validation. The electronic prototype for structural health monitoring applies ultrasound techniques by means of piezoelectric transducers. It is lightweight, has USB 2.0 connectivity and includes data pre-processing algorithms to improve its performance. It can run in pitch-catch and pulse-echo modes employing passive and active techniques. Passive techniques are used to detect impacts or fiber breakage in composite materials. Tests based on active techniques can bring to light several types of damages such as those caused abruptly or those produced progressively by corrosion, delamination or fatigue.

Vibration signal separation of rotating cylindrical structure through target-less photogrammetric approach

Vibration analysis is one of the most widely used methodologies for structural health monitoring. For vibration monitoring of structures, contact-type sensors, such as gap sensors, are commonly utilized. However, these devices can add mass loading to a lightweight structure, resulting in degradation in performance of the structure. As an alternative, photogrammetric approaches, such as digital image correlation and point-tracking techniques have been proposed for vibration measurement. Despite the many advantages of image-based techniques applied by non-contact-type sensors, these are ineffective in detecting vibrations at the outer edge of a rotating cylinder-shaped structure because tracking of the speckles or targets on the backside of a structure invisible to a camera is impossible. Furthermore, vibrations occurring in structures are usually attributed to multiple factors; to identify these factors individually, the vibration signals should be detected separately. In this study, we introduce a noncontact-based vibration signal separation technique that can be used to recognize multiple factors that cause a structure to vibrate at different frequency rates. For this purpose, the vibration occurring in a structure is first detected using an edge-based vibration measurement technique. A phase-based motion magnification technique is then used to generate multiple magnified videos at different dominant frequencies. Subsequently, vibrations were detected in multiple magnified videos to separate vibration signals. To demonstrate the effectiveness of the proposed method, quantitative and qualitative assessments were conducted on both simulation and real video datasets containing vibrations with multiple frequency rates. The results generated by this method can be utilized for health monitoring of various types of structures on which targets, necessary for the general photogrammetry approach for monitoring purposes, are not attached. Furthermore, the magnified videos generated in this study can be used for the visualization and mode identification of existing vibration signals at different frequencies in a structure.

Dual-Algorithm Hybrid Method for Riser Structural Health Monitoring Using the Fewest Sensors

This study suggests a novel riser structural health monitoring methodology based on a dual algorithm (DA). In this method, the displacement tracing algorithm first traces the node displacement and tension up to the last sensor position called the target point. Then, the movement and tension at the target point are used for boundary conditions of the finite element (FE) simulator to obtain displacements and stresses below the target point. The developed method is validated through numerical simulations by comparing riser behaviors/stresses from the fully coupled model with those from the proposed method with numerical sensors. For that, a moored FPSO (floating production storage offloading) system with SCR (steel catenary riser) or SLWR (steel lazy-wave riser) is employed. Only three angle sensors are used at the top portion to monitor the entire length of riser. Much simpler forced top oscillation method is also investigated, which only uses riser top movement for running FE simulator, which cannot accurately reproduce the dynamics of the upper portion of riser since real-time wave action is ignored. The developed DA riser monitoring methodology can reproduce the movements and stresses along the entire length within around 5% error regardless of riser shapes and materials.

Advanced structural health monitoring in carbon fiber-reinforced plastic using real-time self-sensing data and convolutional neural network architectures

In this study, advanced structural health monitoring (SHM) using a non-destructive self-sensing methodology was proposed for large-sized carbon fiber-reinforced plastic (CFRP). Cyclic point bending tests were performed on three types of CFRPs. The damage severity identification and localization were classified and investigated using four different convolutional neural network (CNN) architectures. Electrical resistance images were used to train each CNN architecture for damage analysis. An optimized CNN architecture for the damage analysis of CFRPs using electrical resistance data was proposed and compared with traditional damage analysis CNN architectures. The applicability of the proposed SHM methodology was verified by analyzing unseen damage in the CFRPs. This study addresses the limitations of previous self-sensing methods by reducing the number of electrodes, which reduces data complexity and increases the sensible area of CFRPs. Thus, this study successfully designed an efficient SHM methodology with a high accuracy of over 90 % for analyzing CFRP damage, including the severity and location, regardless of the type of carbon fiber and stacking sequence of composite structures that showed high applicability.

Real-Time Fatigue Crack Growth Rate Estimation Methodology for Structural Health Monitoring of Ships

Fatigue experimental setups for studying the crack growth propagation in a high-strength low-alloy DMR 249A ship steel were arranged by loading the specimen with the real sea-state conditions value of 4 for the application of structural health monitoring of ships. The experimental setup consists of a fatigue loading machine, acoustic emission (AE) sensors, and AE nodes for preprocessing of data. In this investigation, a methodology to identify the crack propagation phenomenon in a specimen independent of the AE parameters has been developed. This methodology proves beneficial in identifying the noise and crack information in ship steel by creating the phase portraits of the time domain signal and indicating the same onto the phase portraits. A polynomial regression-based model for estimating the crack growth rate (CGR) in the material has been developed by introducing a new parameter mean of box count (MBC).

Sensitivity and uncertainty analysis for structural health monitoring with crack propagation under random loads: A numerical framework in the frequency domain

AbstractThe quantification of uncertainties in a system and the ability to identify the most influential parameters for a given structural health monitoring (SHM) strategy constitutes an important step for an in‐depth analysis of the problem. It is used in reliability assessment and for the definition of proper inspection intervals. In a recent paper, the authors proposed a methodology for SHM and fatigue life estimations of structures under random loads. This paper expands over those concepts and presents a framework for sensitivity and uncertainty analysis for SHM of structures in the frequency domain. The structure is an Euler–Bernoulli beam, which is modeled with the Ritz method. The conceptual simplicity of the Ritz model keeps the computational cost low. The results showed that the Walker parameter for the crack propagation law, ; the modal damping factor; and the input acceleration are important parameters affecting the variance of the remaining useful life.

Multi-level damage diagnosis on stiffened composite panels based on a damage-uninformative digital twin

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.