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Application of Optical Fiber Sensors for Structural Health and Usage Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 25 April 2025 | Viewed by 23627

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Special Issue Editors

Dr. Raffaella Di Sante

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Department of Industrial Engineering (DIN), University of Bologna, Via Fontanelle 40, 47121 Forlì, Italy
Interests: mechanical and thermal measurements; optical fiber sensors; fiber bragg gratings; Brillouin distributed measurements; Raleigh distributed measurements; laser Doppler vibrometry; structural health monitoring
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Dr. Claudio Sbarufatti

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Politecnico di Milano, Department of Mechanical Engineering, via La Masa 1, 20156, Milan, Italy
Interests: digital-twin; development and application of statistical and numerical methods for the solution of inverse problems in the context of structural anomaly and structural load identification; inverse FEM; physics-informed machine learning; Bayesian inference and Monte-Carlo methods for model updating; damage prognosis of composite structures; sensor network optimization; active and passive impact monitoring; implementation of multifunctional composite materials with self-sensing and self-heating functions
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Dr. Dimitrios Zarouchas

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Structural Integrity & Composites Group, Faculty of Aerospace Engineering, Delft University of Technology, 2624 Delft, HS, The Netherlands
Interests: AI for structures; prognostics; diagnostics; structural health monitoring; intelligent structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optical Fiber Sensors (OFS) have already been employed in a wide variety of Structural Health Monitoring (SHM) applications due to their numerous advantages, such as small size, light weight, immunity to electromagnetic interference, durability and large bandwidth. Nevertheless, as the number of SHM systems grows, new challenges and new opportunities for OFS arise. A large amount of research is currently focusing on the development of novel OFS as well as innovative SHM systems based on OFS networks. Moreover, it is envisioned that OFS combined with the continued rise in the use of Artificial Intelligence (AI) technology and big data, may unlock innovative solutions in the Digital-Twin framework, further contributing to the growing development of SHM techniques and systems.

This Special Issue aims at collecting the latest original and most significant research efforts involving the use of OFS in SHM.

Topics of potential interest include, but are not limited to:

OFS-based SHM applications in aerospace, civil, energy, and mechanical engineering
Development and applications of interferometric, grating-based, and distributed OFS for SHM
Diagnostics and Prognostics based on the use of OFS data
Machine learning strategies for OFS data processing
Development of digital twins integrating OFS

Dr. Raffaella Di Sante
Dr. Claudio Sbarufatti
Dr. Dimitrios Zarouchas
Guest Editors

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Keywords

optical fiber sensors
structural health monitoring
artificial intelligence
digital twin
machine learning
big data
interferometric sensors
grating-based sensors
distributed sensing

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Published Papers (9 papers)

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Research

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34 pages, 28538 KiB

Open AccessArticle

Monitoring of Composite Structures for Re-Usable Space Applications Using FBGs: The Influence of Low Earth Orbit Conditions

by Thibault Juwet, Geert Luyckx, Alfredo Lamberti, Frank Creemers, Eli Voet and Jeroen Missinne

Sensors 2024, 24(1), 306; https://doi.org/10.3390/s24010306 - 4 Jan 2024

Cited by 2 | Viewed by 1522

Abstract

Fiber Bragg grating sensors (FBGs) are promising for structural health monitoring (SHM) of composite structures in space owing to their lightweight nature, resilience to harsh environments, and immunity to electromagnetic interference. In this paper, we investigated the influence of low Earth orbit (LEO) conditions on the integrity of composite structures with embedded optical fiber sensors, specifically FBGs. The LEO conditions were simulated by subjecting carbon fiber-reinforced polymer (CFRP) coupons to 10 cycles of thermal conditioning in a vacuum (TVac). Coupons with embedded optical fibers (OFs) or capillaries were compared with reference coupons without embedded OFs or capillaries. Embedded capillaries were necessary to create in situ temperature sensors. Tensile and compression tests were performed on these coupons, and the interlaminar shear strength was determined to assess the influence of TVac conditioning on the integrity of the composite. Additionally, a visual inspection of the cross-sections was conducted. The impact on the proper functioning of the embedded FBGs was tested by comparing the reflection spectra before and after TVac conditioning and by performing tensile tests in which the strain measured using the embedded FBGs was compared with the output of reference strain sensors applied after TVac conditioning. The measured strain of the embedded FBGs showed excellent agreement with the reference sensors, and the reflection spectra did not exhibit any significant degradation. The results of the mechanical testing and visual inspection revealed no degradation of the structural integrity when comparing TVac-conditioned coupons with non-TVac-conditioned coupons of the same type. Consequently, it was concluded that TVac conditioning does not influence the functionality of the embedded FBGs or the structural integrity of the composite itself. Although in this paper FBG sensors were tested, the results can be extrapolated to other sensing techniques based on optical fibers. Full article

(This article belongs to the Special Issue Application of Optical Fiber Sensors for Structural Health and Usage Monitoring)

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21 pages, 39490 KiB

Open AccessArticle

Laboratory Results of a Real-Time SHM Integrated System on a P180 Full-Scale Wing-Box Section

by Monica Ciminello, Bogdan Sikorski, Bernardino Galasso, Lorenzo Pellone, Umberto Mercurio, Gianvito Apuleo, Daniele Cirio, Laura Bosco, Aniello Cozzolino, Iddo Kressel, Shay Shoham, Moshe Tur and Antonio Concilio

Sensors 2023, 23(15), 6735; https://doi.org/10.3390/s23156735 - 27 Jul 2023

Viewed by 1028

Abstract

The final objective of the study herein reported is the preliminary evaluation of the capability of an original, real-time SHM system applied to a full-scale wing-box section as a significant aircraft component, during an experimental campaign carried out at the Piaggio Lab in Villanova D’Albenga, Italy. In previous works, the authors have shown that such a system could be applied to composite beams, to reveal damage along the bonding line between a longitudinal stiffening element and the cap. Utilizing a suitable scaling process, such work has then been exported to more complex components, in order to confirm the outcomes that were already achieved, and, possibly, expanding the considerations that should drive the project towards an actual implementation of the proposed architecture. Relevant topics dealt with in this publication concern the application of the structural health monitoring system to different temperature ranges, by taking advantage of a climatic room operating at the Piaggio sites, and the contemporary use of several algorithms for real-time elaborations. Besides the real-time characteristics already introduced and discussed previously, such further steps are essential for applying the proposed architecture on board an aircraft, and to increase reliability aspects by accessing the possibility of comparing different information derived from different sources. The activities herein reported have been carried out within the Italian segment of the RESUME project, a joint co-operation between the Ministry of Defense of Israel and the Ministry of Defense of Italy. Full article

(This article belongs to the Special Issue Application of Optical Fiber Sensors for Structural Health and Usage Monitoring)

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24 pages, 6565 KiB

Open AccessArticle

A Model-Assisted Probability of Detection Framework for Optical Fiber Sensors

by Francesco Falcetelli, Nan Yue, Leonardo Rossi, Gabriele Bolognini, Filippo Bastianini, Dimitrios Zarouchas and Raffaella Di Sante

Sensors 2023, 23(10), 4813; https://doi.org/10.3390/s23104813 - 16 May 2023

Cited by 5 | Viewed by 1899

Abstract

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. Full article

(This article belongs to the Special Issue Application of Optical Fiber Sensors for Structural Health and Usage Monitoring)

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29 pages, 7953 KiB

Open AccessArticle

Towards Automatic Crack Size Estimation with iFEM for Structural Health Monitoring

by Daniele Oboe, Dario Poloni, Claudio Sbarufatti and Marco Giglio

Sensors 2023, 23(7), 3406; https://doi.org/10.3390/s23073406 - 23 Mar 2023

Cited by 5 | Viewed by 2355

Abstract

The inverse finite element method (iFEM) is a model-based technique to compute the displacement (and then the strain) field of a structure from strain measurements and a geometrical discretization of the same. Different literature works exploit the error between the numerically reconstructed strains and the experimental measurements to perform damage identification in a structural health monitoring framework. However, only damage detection and localization are performed, without attempting a proper damage size estimation. The latter could be based on machine learning techniques; however, an a priori definition of the damage conditions would be required. To overcome these limitations, the present work proposes a new approach in which the damage is systematically introduced in the iFEM model to minimize its discrepancy with respect to the physical structure. This is performed with a maximum likelihood estimation framework, where the most accurate damage scenario is selected among a series of different models. The proposed approach was experimentally verified on an aluminum plate subjected to fatigue crack propagation, which enables the creation of a digital twin of the structure itself. The strain field fed to the iFEM routine was experimentally measured with an optical backscatter reflectometry fiber and the methodology was validated with independent observations of lasers and the digital image correlation. Full article

(This article belongs to the Special Issue Application of Optical Fiber Sensors for Structural Health and Usage Monitoring)

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10 pages, 3229 KiB

Open AccessArticle

Automated Damage Detection Using Lamb Wave-Based Phase-Sensitive OTDR and Support Vector Machines

by Rizwan Zahoor, Ester Catalano, Raffaele Vallifuoco, Luigi Zeni and Aldo Minardo

Sensors 2023, 23(3), 1099; https://doi.org/10.3390/s23031099 - 18 Jan 2023

Cited by 4 | Viewed by 2030

Abstract

In this paper, we propose and demonstrate a damage detection technique based on the automatic classification of the Lamb wave signals acquired on a metallic plate. In the reported experiments, Lamb waves are excited in an aluminum plate through a piezoelectric transducer glued onto the monitored structure. The response of the monitored structure is detected through a high-resolution phase-sensitive optical time-domain reflectometer (ϕ-OTDR). The presence and location of a small perturbation, induced by placing a lumped mass of 5 g on the plate, are determined by processing the optical fiber sensor data through support vector machine (SVM) classifiers trained with experimental data. The results show that the proposed method takes full advantage of the multipoint sensing nature of the ϕ-OTDR technology, resulting in accurate damage detection and localization. Full article

(This article belongs to the Special Issue Application of Optical Fiber Sensors for Structural Health and Usage Monitoring)

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24 pages, 9714 KiB

Open AccessArticle

Preliminary Results of a Structural Health Monitoring System Application for Real-Time Debonding Detection on a Full-Scale Composite Spar

by Monica Ciminello, Bogdan Sikorski, Bernardino Galasso, Lorenzo Pellone, Umberto Mercurio, Antonio Concilio, Gianvito Apuleo, Aniello Cozzolino, Iddo Kressel, Shay Shoham and Moshe Tur

Sensors 2023, 23(1), 455; https://doi.org/10.3390/s23010455 - 1 Jan 2023

Cited by 7 | Viewed by 2589

Abstract

The present paper reports the outcomes of activities concerning a real-time SHM system for debonding flaw detection based on ground testing of an aircraft structural component as a basis for condition-based maintenance. In this application, a damage detection method unrelated to structural or load models is investigated. In the reported application, the system is applied for real-time detection of two flaws, kissing bond type, artificially deployed over a full-scale composite spar under the action of external bending loads. The proposed algorithm, local high-edge onset (LHEO), detects damage as an edge onset in both the space and time domains, correlating current strain levels to next strain levels within a sliding inner product proportional to the sensor step and the acquisition time interval, respectively. Real-time implementation can run on a consumer-grade computer. The SHM algorithm was written in Matlab and compiled as a Python module, then called from a multiprocess wrapper code with separate operations for data reception and data elaboration. The proposed SHM system is made of FBG arrays, an interrogator, an in-house SHM code, an original decoding software (SW) for real-time implementation of multiple SHM algorithms and a continuous interface with an external operator. Full article

(This article belongs to the Special Issue Application of Optical Fiber Sensors for Structural Health and Usage Monitoring)

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12 pages, 976 KiB

Open AccessArticle

Fiber-Optic Sensors (FOS) for Smart High Voltage Composite Cables—Numerical Simulation of Multi-Parameter Bending Effects Generated by Irregular Seabed Topography

by Monssef Drissi-Habti, Abhijit Neginhal, Sriharsha Manepalli and Valter Carvelli

Sensors 2022, 22(20), 7899; https://doi.org/10.3390/s22207899 - 17 Oct 2022

Cited by 8 | Viewed by 2609

Abstract

Offshore renewable energy requires reliable high-voltage electric power cables to transport electricity to onshore stations. These power cables are critical infrastructures that are shipped to deep seas through shipping and handling operations and, once mounted, must then evolve in extreme conditions (sea, salt, wind, water-pressure, seabed topography, etc.). All of these operations and working conditions can lead to yielding of copper conductors, often resulting in electric shutdown. Indeed, copper is an excellent electric conductor (conductivity), but its mechanical properties are very poor. If any negligence occurs during the shipping and/or handling operations, copper can undergo plasticity, with effects on both mechanical and electric properties. It is therefore of prime importance to establish a reliable structural health-monitoring (SHM) technique that will enable the continuous recording of copper strain and temperature along a cable, and this has been proven using fiber-optic (FOS) sensors, when the phase is under tensile loading. In this prospective article, the scope is to maintain previous simulations and thus show that by the judicious placement of FOS, one can monitor strain and temperature within cables that are submitted to a bending. This article does not aim to deal directly with the case of a cable that undergoes bending on sloppy areas in seabeds. The idea behind the work is to suggest a concept for the use of embedded fiber-optic sensors and to think about all of what remains to be done as research in order to further suggest this technology to cable manufacturers. Full article

(This article belongs to the Special Issue Application of Optical Fiber Sensors for Structural Health and Usage Monitoring)

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20 pages, 7250 KiB

Open AccessArticle

Physics-Informed Data-Driven Prediction of 2D Normal Strain Field in Concrete Structures

by Mauricio Pereira and Branko Glisic

Sensors 2022, 22(19), 7190; https://doi.org/10.3390/s22197190 - 22 Sep 2022

Cited by 4 | Viewed by 1878

Abstract

Concrete exhibits time-dependent long-term behavior driven by creep and shrinkage. These rheological effects are difficult to predict due to their stochastic nature and dependence on loading history. Existing empirical models used to predict rheological effects are fitted to databases composed largely of laboratory tests of limited time span and that do not capture differential rheological effects. A numerical model is typically required for application of empirical constitutive models to real structures. Notwithstanding this, the optimal parameters for the laboratory databases are not necessarily ideal for a specific structure. Data-driven approaches using structural health monitoring data have shown promise towards accurate prediction of long-term time-dependent behavior in concrete structures, but current approaches require different model parameters for each sensor and do not leverage geometry and loading. In this work, a physics-informed data-driven approach for long-term prediction of 2D normal strain field in prestressed concrete structures is introduced. The method employs a simplified analytical model of the structure, a data-driven model for prediction of the temperature field, and embedding of neural networks into rheological time-functions. In contrast to previous approaches, the model is trained on multiple sensors at once and enables the estimation of the strain evolution at any point of interest in the longitudinal section of the structure, capturing differential rheological effects. Full article

(This article belongs to the Special Issue Application of Optical Fiber Sensors for Structural Health and Usage Monitoring)

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24 pages, 6656 KiB

Open AccessReview

Fiber Optic Sensors Embedded in Textile-Reinforced Concrete for Smart Structural Health Monitoring: A Review

by Lourdes S. M. Alwis, Kort Bremer and Bernhard Roth

Sensors 2021, 21(15), 4948; https://doi.org/10.3390/s21154948 - 21 Jul 2021

Cited by 39 | Viewed by 6062

Abstract

The last decade has seen rapid developments in the areas of carbon fiber technology, additive manufacturing technology, sensor engineering, i.e., wearables, and new structural reinforcement techniques. These developments, although from different areas, have collectively paved way for concrete structures with non-corrosive reinforcement and in-built sensors. Therefore, the purpose of this effort is to bridge the gap between civil engineering and sensor engineering communities through an overview on the up-to-date technological advances in both sectors, with a special focus on textile reinforced concrete embedded with fiber optic sensors. The introduction section highlights the importance of reducing the carbon footprint resulting from the building industry and how this could be effectively achieved by the use of state-of-the-art reinforcement techniques. Added to these benefits would be the implementations on infrastructure monitoring for the safe operation of structures through their entire lifespan by utilizing sensors, specifically, fiber optic sensors. The paper presents an extensive description on fiber optic sensor engineering that enables the incorporation of sensors into the reinforcement mechanism of a structure at its manufacturing stage, enabling effective monitoring and a wider range of capabilities when compared to conventional means of structural health monitoring. In future, these developments, when combined with artificial intelligence concepts, will lead to distributed sensor networks for smart monitoring applications, particularly enabling such distributed networks to be implemented/embedded at their manufacturing stage. Full article

(This article belongs to the Special Issue Application of Optical Fiber Sensors for Structural Health and Usage Monitoring)

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