Search Results (76)

Stay cables are critical components of cable-stayed bridges, and their dynamic characteristics provide essential information for vibration assessment and structural health monitoring. This study presents a comparative performance assessment of Fiber Bragg Grating (FBG) sensors and PCB accelerometers for field-based dynamic characterization of stay cables. Field measurements were conducted on selected stay cables of My Thuan Bridge under normal operating conditions. Since PCB accelerometers measure acceleration, whereas FBG sensors capture dynamic strain or wavelength-shift responses, the comparison was performed primarily in the frequency domain rather than through direct time-domain amplitude equivalence. The measured responses were processed using signal preprocessing, power spectral density analysis, and covariance-driven stochastic subspace identification. Dominant dynamic frequencies were identified from both sensing systems and compared using frequency agreement, frequency deviation, spectral peak consistency, and practical field applicability. The results show that the common frequencies identified from FBG and PCB measurements are in good agreement, with an overall mean relative difference of 0.94%. The FBG sensors also detected additional candidate frequency components that showed an approximately regular progression consistent with stay-cable vibration, although these components require further validation. These findings indicate that FBG sensing can provide reliable and complementary frequency-domain information for stay-cable dynamic characterization. The study demonstrates the feasibility of using FBG sensors as an alternative or complementary sensing technique to conventional accelerometers for field-based vibration monitoring of cable-stayed bridges. Full article

In cardiovascular interventional procedures, real-time, precise monitoring of minute strain and temperature fluctuations at the catheter tip is essential to improving both the safety and efficacy of these interventions. Fiber Bragg grating (FBG)-based sensors present a promising solution owing to their diminutive size and immunity to electromagnetic interference; however, the inherent cross-sensitivity between strain and temperature remains a significant obstacle. This paper introduces a dual-FBG fiber optic sensing structure that leverages machine learning techniques. The system incorporates two FBGs: one set acts as the primary sensing element, positioned within a simulated catheter and affixed to the substrate under examination with cyanoacrylate adhesive to detect composite strain and temperature signals; the second set is spirally wound around the catheter surface to solely measure temperature, thus effectively isolating temperature interference. Additionally, a machine learning model is employed to learn the nonlinear mapping between the recorded FBG spectra and the actual strain and temperature parameters. Experimental validation was conducted within the physiologically relevant temperature range of 20 °C to 45 °C. The findings indicate that the proposed machine learning model can successfully decouple strain and temperature, achieving high-precision predictions even in situations where the sensing unit exhibits a slight nonlinear response due to adhesive bonding. This study substantiates the feasibility of utilizing machine learning-enhanced dual-FBG structures for multi-parameter sensing in complex environments. The proposed methodology presents a promising avenue for the development of next-generation smart optical fiber sensors intended for application in catheter systems. Full article

The increasing complexity and extended operational lifetimes of modern space infrastructure have significantly intensified the demand for reliable structural health monitoring (SHM) systems. However, the extreme space environment, characterized by radiation exposure, microgravity, ultra-high vacuum, and severe thermal cycling, imposes critical limitations on conventional electrical sensing technologies, leading to reduced measurement accuracy, instability, and long-term degradation. This review presents a comprehensive analysis of fiber Bragg grating (FBG)-based sensing technologies as a promising solution for deformation monitoring in space infrastructure. The study investigates the fundamental operating principles of FBG sensors under space conditions and systematically classifies existing FBG-based SHM architectures, including point-based, multiplexed, long-distance, and hybrid sensing systems. Furthermore, the advantages of FBG sensors—such as immunity to electromagnetic interference, passive operation, and high-resolution multipoint sensing—are critically evaluated in comparison with traditional electrical sensors. In addition, key challenges affecting the performance of FBG systems in space environments are analyzed, including radiation-induced wavelength drift, temperature–strain cross-sensitivity, signal attenuation, and long-term stability issues. The paper also highlights recent advances in interrogation techniques and network architectures that enable reliable in situ and real-time deformation monitoring of space structures. The results demonstrate that FBG-based sensing systems provide a scalable and robust framework for SHM in extreme environments while also revealing existing limitations and open research challenges. This work establishes a structured foundation for the development of next-generation intelligent monitoring systems for space infrastructure. Full article

Distributed fiber optic sensing (DFOS) has emerged as a critical technology for structural health monitoring of large-scale infrastructure, offering unique advantages in terms of coverage and environmental adaptability. This review presents a comprehensive analysis of the two dominant technical routes: fully distributed sensing based on intrinsic backscattering and massive-capacity sensing based on ultra-weak fiber Bragg grating (UWFBG) networks. For backscattering-based systems—encompassing Raman, Brillouin, and Rayleigh scattering—the inherent trade-offs among signal-to-noise ratio (SNR), spatial resolution, and sensing range constitute major performance bottlenecks. This review systematically summarizes advanced demodulation and signal processing strategies designed to overcome these physical barriers, including pulse coding sequences, chaotic laser compressed correlation, and deep learning-enhanced noise reduction algorithms. In parallel, for UWFBG-based technologies, the evolution from traditional multiple-point fiber Bragg grating (FBG) array to quasi-distributed and fully distributed UWFBG network is discussed. This review highlights key breakthroughs in achieving high spatial resolution and high-speed interrogation through hybrid multiplexing, aliased spectrum reconstruction, and dispersion-based demodulation techniques. By synthesizing recent advances in modulation schemes, detection hardware, and algorithmic processing, this paper outlines the trajectory of DFOS technologies toward high-precision, long-distance, and real-time sensing networking. Full article

Fiber Bragg gratings (FBGs) have evolved from passive sensing elements into actively programmable photonic components, enabling dynamic wavelength control across diverse applications. This review provides a comprehensive and systematic overview of active wavelength control technologies for FBGs, deliberately excluding passive sensing applications. We systematically categorize the fundamental tuning mechanisms—including mechanical, thermal, optothermal, electro-optic, nonlinear optical, and hybrid approaches—and compare their performance characteristics in terms of tuning range, speed, precision, and trade-offs. Key enhancement techniques, such as mechanical amplification, thermal packaging, femtosecond laser fabrication, and FPGA-based interrogation, are examined. The transformative impact of actively controlled FBGs is elucidated across three major application domains: tunable and narrow-linewidth fiber lasers, reconfigurable microwave photonic systems, and emerging fields including quantum information processing and biomedical imaging. A consolidated technology map visualizes the connections between enabling techniques and applications. Finally, we critically analyze core challenges—performance trade-offs, control complexity, and integration bottlenecks—and outline future research directions driven by novel materials, artificial intelligence, and quantum technologies. This review offers a structured framework for understanding active FBGs as programmable photonic primitives, providing actionable insights for researchers and engineers in academia and industry. Full article

Optical-based sensors have been widely used for various applications, including biosensing, civil structural health monitoring, and humidity sensing. Fiber Bragg Grating (FBG) is one of the optical-based sensing approaches that are commonly used in these applications. The effect of hygroscopic coating on different grating profiles, including uniform, apodized, and tilted FBG, was investigated by connecting the FBG to a light source and optical spectrum analyzer (OSA). The reflected wavelength of the FBG, captured by the OSA at increasing and decreasing relative humidity ranging from 40 to 80% RH were recorded. Tin dioxide (SnO₂) is one of the metal oxides with a hygroscopic nature, which is suitable to act as a coating material for FBG. For improved sensitivity, the HMT-enhanced technique was applied in this study to modify the surface morphology of SnO₂, increasing the porosity of the nanostructure for water adsorption and desorption. The result showed that the sensitivity and linearity of the FBG-based humidity sensor can be enhanced via HMT-enhanced SnO₂ nanostructure coating onto uniform FBG. A sensitivity of 1.30 pm/%RH and 1.52 pm/%RH was reported during incremental and decremental RH, respectively, and a fitting coefficient of >0.97 was recorded. This approach demonstrated the feasibility of hygroscopic coating on FBG to enable broad applications across various humidity sensing industries such as agriculture, pharmaceutical, and semiconductors. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

Optical fiber sensors (OFSs) have emerged as essential tools in the monitoring of physical, chemical, and bio-medical parameters in harsh situations due to their high sensitivity, electromagnetic interference (EMI) immunity, and long-term stability. However, the current literature contains scattered information in most reviews regarding individual sensing technologies or domains. This study provides a structured exploratory review in a novel inter-family analysis of both intrinsic and extrinsic configurations by analyzing more than 23,000 publications between 2019 and 2025 in five key domains: industry, medicine and biomedicine, environmental chemistry, civil/structural engineering, and aerospace. The analysis aims to critically discuss how functional principles/parameters and methods of interrogation affect the applicability of different OFS categories. The results reveal leading trends in the use of techniques like the use of fiber Bragg gratings (FBG) and distributed sensing in high-accuracy conditions or the rising role of extrinsic sensors in selective chemical situations and point out new approaches in areas like Artificial Intelligence (AI)- or Internet of Things (IoT)-integrated sensors. Further, this synthesis not only connects pieces of knowledge but also defines the technological barriers in terms of calibration cost and standardization: this provides strategic insight regarding future research and the scalability of industry deployment. Full article

Optical fiber biosensors have evolved into powerful tools for non-invasive biomedical analysis. While foundational principles are well-established, recent years have marked a paradigm shift, driven by advancements in nanomaterials, fabrication techniques, and data processing. This review provides a focused overview of these emerging trends, critically analyzing the innovations that distinguish the current generation of optical fiber biosensors from their predecessors. We begin with a concise summary of fundamental sensing principles, including Surface Plasmon Resonance (SPR) and Fiber Bragg Gratings (FBGs), before delving into the latest breakthroughs. Key areas of focus include integrating novel 2D materials and nanostructures to dramatically enhance sensitivity and advancing synergy with Lab-on-a-Chip (LOC) platforms. A significant portion of this review is dedicated to the rapid expansion of clinical applications, particularly in early cancer detection, infectious disease diagnostics, and continuous glucose monitoring. We highlight the pivotal trend towards wearable and in vivo sensors and explore the transformative role of artificial intelligence (AI) and machine learning (ML) in processing complex sensor data to improve diagnostic accuracy. Finally, we address the persistent challenges—biocompatibility, long-term stability, and scalable manufacturing—that must be overcome for widespread clinical adoption and commercialization, offering a forward-looking perspective on the future of this dynamic field. Full article

The increasing deployment of photovoltaic (PV) installations presents critical challenges related to module safety and efficiency. Early detection of hotspots on PV modules is crucial to prevent degradation and mitigate fire risk. This study investigates the effectiveness of fiber optic sensors, specifically fiber Bragg gratings (FBGs) and distributed temperature sensing (DTS) based on Brillouin backscattering, to detect and localize hotspots on PV modules. Both sensor types successfully identified hotspot occurrences, with validation conducted through simultaneous thermocouple measurements and infrared thermographic imaging. The tests provide a comprehensive analysis of measurement methodologies, highlighting the advantages and limitations of fiber optic sensing techniques. While FBG sensors offer the most precise temperature measurements at the PV module surface, DTS systems demonstrate superior capability in hotspot detection. Full article

Mode conversion effects in Fibre Bragg Gratings (FBGs) are widely exploited in applications such as sensing and fibre lasers. However, when FBGs are inscribed into Few-mode optical Fibres (FMFs), the mode interactions become highly complex due to the increased number of guided modes, rendering their practical use difficult. In this study, we investigate whether the addition of a spatial mode multiplexer, used to selectively excite specific fibre modes, can simplify the interpretation and utility of few-mode FBGs (FM-FBGs). We focus on point-by-point (PbP)-inscribed FBGs, localised with respect to the transverse cross-section of the fibre core, and study their interaction with a range of Hermitian Gauss input modes. We present a comprehensive numerical study supported by experimental validation, examining the mechanisms of mode coupling induced by localised FBGs and its implications, with a focus on sensing applications. Our results show that the introduction of a spatial mode multiplexer leads to slight simplification of the FBG transmission spectrum. Nevertheless, significant simplification of the reflection spectrum is achievable after modal filtering occurs as the reflected light re-traverses the spatial mode multiplexer, potentially enabling WDM monitoring of FM-FBGs. Notably, we report a novel approach to multiplexing FBGs based on their transverse location within the fibre core and the modal content initially coupled into the fibre. To the best of our knowledge, this multiplexing technique is yet to be reported. Full article

Machine learning (ML)-based techniques have received significant attention in various fields of industry and science. In civil and bridge engineering, they can facilitate the identification of specific patterns through the analysis of data acquired from structural health monitoring (SHM) systems. To evaluate the prediction capabilities of ML, this study examines the performance of several ML algorithms in estimating the total weight and location of vehicles on a bridge using strain sensing. A novel framework based on a combined model and data-driven approach is described, consisting of the establishment of the finite element (FE) model, its updating according to load testing results, and data augmentation to facilitate the training of selected physics-informed regression models. The article discusses the design of the Fiber Bragg Grating (FBG) sensor-based Bridge Weigh-in-Motion (BWIM) system, specifically focusing on several supervised regression models of different architectures. The current work proposes the use of the updated FE model to generate training data and evaluate the accuracy of regression models with the possible exclusion of selected input features enabled by the structural specificity of a bridge. The data were sourced from the SHM system installed on a network arch bridge in Wolin, Poland. It confirmed the possibility of establishing the BWIM system based on strain measurements, characterized by a reduced number of sensors and a satisfactory level of accuracy in the estimation of loads, achieved by exploiting the network arch bridge structural specificity. Full article

Carbon fiber-reinforced polymer (CFRP) laminates have been widely coated on aged and damaged structures for recovering or enhancing their structural performance. The health conditions of the coated composite structures have been given high attention, as they are critically important for assessing operational safety and residual service life. However, the current problem is the lack of an efficient, long-term, and stable monitoring technique to characterize the structural behavior of coated composite structures in the whole life cycle. For this reason, bare and packaged fiber Bragg grating (FBG) sensors have been specially developed and designed in sensing networks to monitor the structural performance of CFRP-coated composite beams under different loads. Some optical fibers have also been inserted in the CFRP laminates to configure the smart CFRP component. Detailed data interpretation has been conducted to declare the strengthening process and effect. Finite element simulation and simplified theoretical analysis have been conducted to validate the experimental testing results and the deformation profiles of steel beams before and after the CFRP coating has been carefully checked. Results indicate that the proposed FBG sensors and sensing layout can accurately reflect the structural performance of the composite beam structure, and the CFRP coating can share partial loads, which finally leads to the downward shift in the centroidal axis, with a value of about 10 mm. The externally bonded sensors generally show good stability and high sensitivity to the applied load and temperature-induced inner stress variation. The study provides a straightforward instruction for the establishment of a structural health monitoring system for CFRP-coated composite structures in the whole life cycle. Full article