Search Results (1,153)

The oil-immersed environment within power transformers is characterized by high temperatures, strong electric fields, and severe electromagnetic interference, posing significant challenges for simultaneous multi-parameter monitoring. Conventional electrical sensors are susceptible to electromagnetic interference, whereas typical integrated fiber Bragg grating (FBG) sensors exhibit cross-sensitivity and reliability issues under such harsh operating conditions. To address these challenges, this paper proposes an integrated FBG-based sensor. Through specialized material and structural design, each sensing element is engineered to respond predominantly to its target parameter at the physical level. This approach effectively mitigates cross-sensitivity, enabling high-precision simultaneous measurement of oil temperature, pressure, and moisture content. Under simulated transformer oil conditions, the sensor achieved a temperature sensitivity of 17.1 pm/°C, a pressure sensitivity of approximately 4 nm/MPa, and a moisture sensitivity of 7.775 × 10⁻⁴ nm/%RS (equivalent to 6.37 × 10⁻⁴ nm/ppm at 40 °C). The results also confirmed excellent linearity, repeatability, and resistance to cross-sensitivity. These findings demonstrate that the proposed integrated FBG sensor can achieve stable multi-parameter measurement and effective decoupling under the tested transformer-oil conditions, indicating its potential for engineering application in transformer online monitoring. Full article

In the field of shock physics and energetic materials, Fiber Bragg Gratings (FBGs) are used to measure shock velocity, detonation velocity and shock pressure levels. They are also used to measure strain in structures loaded with explosive effects. FBG sensors are known to be light, small, immune electromagnetic environments and have fast response compared to electrical sensors. To use one or more gratings along a fiber, a high-resolution spectrometer with a high sampling rate has been developed. This dynamic spectrometer employs time-multiplexing by wavelength-to-time conversion using dispersion. It provides a complete view of the spectra evolution at a rate of 100 MHz. Thus, complex phenomena can be observed. In this paper, the interrogation technique is presented in more detail, and experimental results are discussed. The experiments presented are a low-pressure shock velocity measurement in epoxy, a deflagration-to-detonation transition in a porous energetic material, a Shock-to-Detonation Transition in a dense energetic material, a tentative-to-measure pressure level in epoxy from an FBG made in a sapphire fiber and multi-point strain measurements up to eight FBGs. The advantages and limits are discussed for each type of experiment. Full article

To resolve the discrepancy between the measured strain and the actual surface strain of wind turbine blades when using rubber-encapsulated fiber Bragg grating (FBG) sensors for strain monitoring, this study establishes a surface-bonded strain transfer model for such sensors. The total strain transfer efficiency of the sensor is decomposed into two components: the strain transfer efficiency from the rubber substrate to the FBG core (encapsulated grating strain transfer efficiency) and that from the wind turbine blade to the rubber substrate (strain transfer efficiency between the rubber substrate and the blade). Based on the theory of mechanics of materials, the strain transfer equation is derived, and the key factors influencing strain transfer efficiency—FBG bonding length and rubber substrate thickness—are analyzed via the control variable method. Three ethylene propylene diene monomer (EPDM)-encapsulated FBG sensors with rubber substrate thicknesses of 3 mm, 4 mm, and 6 mm were fabricated. Tensile strain transfer tests were conducted using fiber-reinforced plastic (FRP) strips to simulate the material properties of wind turbine blades, so as to validate the effectiveness of the proposed model. The experimental results demonstrate that the strain transfer efficiency of the sensor increases with the extension of FBG bonding length and decreases with the increase in rubber substrate thickness, with 4 mm determined as the optimal substrate thickness for EPDM-encapsulated FBG sensors. On the basis of the aforementioned findings, an EPDM-encapsulated FBG strain rosette sensor was developed, which can effectively measure the complex stress of a wind turbine blade model. This study provides a theoretical foundation for the structural design and engineering application of rubber-encapsulated FBG sensors in the strain monitoring of wind turbine blades. Full article

When traditional geogrids are used to mitigate reflective cracks in asphalt pavement, it is difficult to monitor the internal state of the pavement and the strain of the geogrid in real time. This study proposes a sensing geogrid based on warp-knitting technology, where fiber Bragg grating (FBG) sensors are embedded into the geogrid through the weaving process, enabling it to possess both reinforcement and strain-sensing functions. The sensing geogrid was calibrated through laboratory tensile tests, and field monitoring was conducted to obtain optical signal variation data at various stages during asphalt pavement paving, as well as the deformation of the geogrid at different measurement points in each stage. The results indicate that the weaving process did not damage the FBG sensors, and the sensing geogrid exhibited good optical signal performance and normal signal acquisition during the production and transportation stages. The strain of the FBG sensors and the geogrid showed a linear correlation, with a correlation coefficient of 845 με/nm, demonstrating good deformation compatibility between them. Field monitoring confirmed that the sensing geogrid has good construction adaptability and can perceive fluctuations in optical signals and deformation of the geogrid during the construction process. Specifically, significant deformation of the geogrid occurred during the construction of the asphalt-treated base (ATB-25) and bottom layers, accompanied by substantial fluctuations in optical signals due to construction machinery. In contrast, signal fluctuations were smaller during the construction of the middle and surface layers, with the influence depth of construction machinery being approximately 22 cm. Compared to ordinary road sections, the deflection basin curve of the reinforced section was gentler, and the maximum deflection was reduced by approximately 41%. This study confirms the feasibility of the sensing geogrid and provides a valuable reference for its application in road engineering. Full article

This study presents an experimental and numerical investigation on the impact of embedded fiber optic sensors on the mechanical properties, like tensile, compression, bending and compression-after-impact properties, and sensing performances of intelligent composites. The influence by different volume fractions of embedded fiber optics on the mechanical properties was revealed. Combined with finite element simulations, the effect of embedded sensors on the basic mechanical properties of composite materials was obtained. The sensing performance of the embedded fiber Bragg grating (FBG) sensors was validated through comparison with conventional strain gauges. Full article

This study demonstrates single-channel fiber Bragg grating (FBG) sensing for relative vibration-state monitoring of a motor–support system under angle-dependent boundary conditions. A packaged FBG accelerometer-type sensing unit was mounted on the motor–support structure, and the reflected Bragg wavelength was recorded as a one-dimensional optical vibration response. Because the sensor was installed away from the rotating shaft, the measured wavelength fluctuation was interpreted as a coupled vibration-sensitive response of the motor, fixture, sensor package, bonding condition, and changing boundary state, rather than as a calibrated shaft speed or absolute acceleration signal. Adaptive variational mode decomposition (AVMD) was applied to track the time-varying narrowband spectral-response trajectory of the Bragg-wavelength signal. In parallel, raw wavelength windows were supplied to LSTM, 1D-CNN, and CNN–LSTM autoencoders to evaluate waveform departures from learned nominal fixed-angle behavior. The fixed-angle results showed stable but distinguishable optical vibration responses under different boundary states, whereas the dynamic angle-transition records produced local trajectory changes and alarm-candidate intervals. Baseline and autoencoder comparisons further clarified the trade-off between transition coverage and false-alarm tendency. The RMS threshold baseline was more sensitive to transition-related amplitude changes but produced more false alarms, whereas the CNN–LSTM autoencoder provided the most selective response among the tested autoencoder branches. The results are interpreted as task-specific evidence for relative vibration-state transition monitoring rather than as general motor fault diagnosis. Overall, the framework demonstrates a compact FBG-based route for relative vibration-state transition monitoring when speed references, dense sensor layouts, and labeled fault data are unavailable. Full article

Accurate state-of-charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries. However, the weak voltage observability of lithium iron phosphate (LFP) batteries within the voltage plateau region limits the accuracy of conventional voltage-based methods. To address this issue, an electro–mechanical information fusion framework for SOC estimation is proposed. Fiber Bragg grating (FBG) sensors were employed to simultaneously measure the surface strain and temperature of prismatic LFP batteries. Experimental results showed that the strain signal exhibited a stronger correlation with SOC than the voltage signal, with an average absolute correlation coefficient of 0.92. A Thevenin equivalent circuit model combined with an adaptive forgetting factor recursive least squares (AFFRLS) algorithm was established for online voltage modeling, while a Mamba-based strain model was developed to capture the nonlinear temporal relationship between multidimensional sensing data and battery strain. The two models were further integrated with adaptive unscented Kalman filters (AUKFs) and fused through a dual-layer adaptive weighting strategy. Experimental results under the five operating conditions considered in this study demonstrated that the proposed method achieved average RMSE and MAE values of 0.98% and 0.80%, respectively, outperforming standalone voltage- and strain-based methods. Full article

Structural health monitoring (SHM) is a component of modern civil engineering. This review analyzes the integration of sensing technologies and artificial-intelligence-based methods for damage detection, localization, classification, prognosis, and anomaly detection in buildings and civil infrastructure. The database search covered Web of Science (WoS), Scopus, and IEEE Xplore for the period 1 January 2020–31 December 2025. The initial records were 292 in WoS, 311 in Scopus, and 338 in IEEE Xplore; after applying the AI-related search constraint, the corresponding AI-SHM corpora were 71, 79, and 139 records, respectively. The combined screening and eligibility workflow produced 31 open-access studies for detailed qualitative analysis, while the task-specific performance tables synthesize the subset of studies for which the sensor type, AI model, SHM task, validation context, and performance metrics were explicitly reported. The review, therefore, interprets reported performance by SHM task and sensor modality, rather than treating heterogeneous metrics as directly comparable across different datasets and experimental conditions. The results indicate that high values reported for accelerometer-, fiber-optic-, piezoelectric transducer-, and vision-based systems are mainly obtained under controlled, benchmark, simulated, or study-specific validation conditions. Consequently, robustness, transferability to operational structures, uncertainty quantification, sensor-network design, and integration with Physics-Informed Machine Learning and Digital Twin technologies remain central research needs. Full article

The UHV dry-type bushing plays a critical role in power transmission by enabling electrical connection, electrical insulation, and mechanical support, making it a core component for ensuring the safe and stable operation of UHV direct current (DC) transmission projects. Epoxy resin, serving as the fundamental insulating material for the bushing core, undergoes significant residual strain during high-temperature curing due to chemical shrinkage and thermal strain, which directly affects the molding quality and service reliability of the component. This paper investigates the curing process of a large-thickness epoxy material, which is on the same scale as a UHV bushing. An in situ monitoring system combining fiber Bragg grating (FBG) sensors and thermocouples, together with COMSOL Multiphysics simulations, is employed to systematically study the evolution of the temperature field and residual strain throughout the entire curing process, considering the demolding effect. The results show that during the curing stage, the internal temperature distribution is non-uniform, with a maximum temperature difference of 65 °C between the center and the edge. The residual strain is dominated by chemical shrinkage (accounting for 73.25%) and exhibits a pronounced radial gradient. Mold constraint and demolding cause abrupt changes in the strain. The developed thermo-chemo-mechanical coupled model shows good agreement between simulations and experimental measurements. Thermal cycling relaxes the residual stress, achieving a reduction of 3.89–5.77%. This study provides support for process optimization and defect prevention in large-scale epoxy insulation components. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

Accurately characterizing the accumulated plastic deformation of the cement sheath is essential for evaluating wellbore integrity. Fiber Bragg Grating (FBG) technology, noted for its strong immunity to external interference, is employed for in situ monitoring under harsh downhole conditions. This study investigates the accumulated plastic deformation behavior of set cement through uniaxial cyclic loading–unloading tests and proposes a real-time, high-precision, and non-destructive monitoring scheme by integrating FBG sensors into the Casing–Cement–Formation system (CCFS). The results reveal that under uniaxial conditions, cumulative plastic strain increases with stress amplitude, with the plastic strain in a single cycle capable of reaching up to 0.2%. Under identical conditions, FBG measurements exhibit a drift phenomenon, resulting in an error margin of approximately 1.5–2%. Furthermore, within the CCFS, plastic strain exhibits linear accumulation during the initial 2–5 cycles, followed by a deceleration in the accumulation rate. This deceleration is attributed to the redistribution of internal stress induced by plastic strain accumulation. Notably, the addition of silica fume and latex significantly mitigates this deformation. Collectively, these findings validate the effectiveness of FBG technology for downhole integrity assessment and offer a pathway for early failure detection and targeted maintenance. 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

Wind turbine blades serve as the core components of wind energy conversion systems, and their safe and stable operation is pivotal to the operational efficiency and reliability of wind farms. However, prolonged operation in harsh environmental conditions such as strong winds, heavy rainfall, ultraviolet radiation, and temperature fluctuations renders wind turbine blades susceptible to fatigue damage and structural failure. Aiming at the drawbacks of traditional electromagnetic sensors, including their vulnerability to lightning strikes and poor corrosion resistance, as well as the elastic modulus mismatch between existing fiber Bragg grating (FBG)-encapsulated sensors and wind turbine blade structures, this study selects the ethylene–propylene–diene monomer (EPDM) as the encapsulation material to develop EPDM-FBG strain sensors. The effectiveness of the proposed sensor in blade strain monitoring is ultimately verified via static load model tests on small-scale wind turbine blades. Test results demonstrate that the EPDM-FBG strain sensor exhibits excellent static strain sensing performance, with its test results being highly consistent with those of bare FBG sensors and a relative error of less than 5%, which can fully meet the practical requirements of static strain monitoring for wind turbine blades. This research provides a novel and reliable monitoring method for the health monitoring of wind turbine blades. Full article

Fiber Bragg Grating (FBG) sensors are widely used for strain and temperature monitoring due to their high sensitivity, compact size, electromagnetic immunity, and multiplexing capability. While conventional FBG interrogators remain bulky and costly, Photonic Integrated Circuit (PIC) platforms provide a promising route toward compact, scalable, and low-power FBG interrogation. However, the choice of architecture strongly determines the achievable resolution, bandwidth, multiplexing capacity, and robustness. This review compares on-chip demodulation architectures, evaluating their performance in resolution, bandwidth, and interrogation speed. We show that the optimal architecture depends strongly on the application: AWG-based schemes excel in compact, multi-FBG readout; ring-resonator systems are highly effective for tunable filtering; and interferometric phase-domain schemes offer the highest sensitivity for dynamic strain sensing. Despite these architectural advances, practical deployment remains constrained by system-level bottlenecks. These challenges primarily include source/detector integration, fiber–chip coupling, packaging robustness, and thermal drift. Overcoming these barriers requires a shift in future development from isolated photonic-device optimization toward comprehensive, system-level co-design. Full article

Structural damage classification in composite UAV wings is a key challenge in Structural Health Monitoring (SHM), particularly under barely visible impact damage conditions. Fiber Bragg Grating (FBG) sensor networks provide high-resolution strain data; however, systematic experimental benchmarking of lightweight neural architectures trained on real FBG datasets remains limited, especially under sensor degradation scenarios. This work presents a four-phase benchmarking study of Multilayer Perceptron (MLP) configurations using strain measurements from a composite UAV wing instrumented with 32 FBG sensors across five damage states and 210 loading experiments. The framework evaluates optimization strategies, hyperparameter sensitivity, architectural depth, and robustness under controlled sensor dropout, Gaussian noise, and wavelength drift perturbations. Results indicate that compact architectures with progressive dimensional reduction (256–128–64) trained using adaptive optimizers (AdamW and Nadam) achieve the best balance between macro-F1 performance (up to 0.85 during validation), stability, and computational efficiency. Robustness analysis shows gradual performance degradation under sensor loss, suggesting distributed strain-field learning. These findings provide practical guidelines for selecting computationally efficient and robust neural models for deployable FBG-based SHM systems in aerospace applications. Full article