Search Results (2,453)

Temperature is the key monitoring measurement of lithium-ion battery condition monitoring, and it plays a very important role in battery life prediction, thermal runaway warning, and thermal management decision making. Therefore, this paper mainly summarizes the research status of internal temperature monitoring (ITM) method for lithium-ion batteries. Firstly, the lithium-ion battery ITM methods are divided into three types, namely temperature sensor, battery thermal model, and electrochemical impedance spectroscopy (EIS) types. The measurement principle, implementation difficulty, and measurement effect of the above ITM methods are different. The advantages and disadvantages of these methods are analyzed and pointed out. In particular, the five latest ITM methods based on negative temperature coefficient (NTC) temperature sensor, optical fiber sensor, ultrasonic wireless sensor, electrochemical thermal coupling model, and multi-frequency EIS are introduced. Finally, based on the existing research, the future development trend of the above five methods is discussed. Full article

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a low-cost alternative; however, their lower accuracy and sensor bias issues, particularly in maritime environments, remain considerable obstacles. This study proposes an improved method for bias estimation by comparing the estimated values from a trajectory generator (TG)-based acceleration and angular-velocity estimation system with actual measurements. Additionally, for X- and Y-axis accelerations, we introduce a method that leverages the correlation between altitude differences derived from an INS/GNSS/gyrocompass (IGG) and those obtained during the TG estimation process to estimate the bias. Simulation datasets from experimental voyages validate the proposed method by evaluating the mean, median, normalized cross-correlation, least squares, and fast Fourier transform (FFT). The Butterworth filter achieved the smallest angular-velocity bias estimation error. For X- and Y-axis acceleration bias, altitude-based estimation achieved differences of 1.2 × 10⁻² m/s² and 1.0 × 10⁻⁴ m/s², respectively, by comparing the input bias using 30 min data. These methods enhance the positioning and attitude estimation accuracy of low-cost IMUs, providing a cost-effective maritime navigation solution. Full article

Bracing is a widely used conservative treatment for adolescent idiopathic scoliosis (AIS) patients, yet there is no consensus on the optimal amount of force applied. Although a number of different sensors have been developed to continuously monitor the applied pressure and force, they have several limitations, including inadequate overall force distribution and displacement. They also cause discomfort with limited wearability. In this study, body pressure mapping knitwear (BPMK) integrated with fourteen silicone-embedded fiber Bragg grating (FBG) sensors is developed to monitor immediate and overall changes in force during the bracing treatment. A wear trial of the BPMK is conducted by using a validated soft AIS mannequin, and prediction equations have been formulated for the FBG sensors at individual locations. The findings indicate that the measured forces are in good agreement with those obtained from clinical studies, with peak forces around the padding regions reaching approximately 2N. This was further validated by using finite element (FE) models. When comparing X-ray images, the estimated differences in Cobb angles were found to be 0.6° for the thoracic region and 2.1° for the lumbar region. This model is expected to provide valuable insights into optimal force application, thus minimizing the risk of injury and enhancing bracing compliance and efficacy. Ultimately, this innovative approach provides clinicians with data-driven insights for safer and more effective bracing applications, thus improving the quality of life of AIS patients. Full article

The piezoelectric grating voltage sensor has garnered significant attention in the realm of intelligent sensing, attributed to its compact size, cost-effectiveness, robust electromagnetic interference (EMI) immunity, and high network integration capabilities. In this paper, we propose a PZT-FBG (piezoelectric ceramic–fiber Bragg grating) voltage–temperature demodulation optical path architecture. This scheme effectively utilizes the originally unused temperature compensation reference grating, repurposing it as a temperature measurement grating. By employing FBGs with identical or similar parameters, we experimentally validate two distinct optical path connection schemes, before and after optimization. The experimental results reveal that, when the input voltage ranges from 250 V to 1800 V at a frequency of 50 Hz, the goodness of fit for the three fundamental waveforms is 0.996, 0.999, and 0.992, respectively. Furthermore, the sensor’s frequency response was tested across a frequency range of 50 Hz to 20 kHz, demonstrating that the measurement system can effectively respond within the sensor’s operational frequency range. Additionally, temperature measurement experiments showed a goodness of fit of 0.997 for the central wavelength of the FBG as the temperature increased. This research indicates that the improved optical path connection method not only accomplishes a synchronous demodulation of both temperature and voltage parameters but also markedly enhances the linearity and resolution of the voltage sensor. This discovery offers novel insights for further refining sensor performance and broadening the applications of optical voltage sensors. Full article

A sensitivity-enhanced temperature sensor based on a Mach–Zehnder interferometer (MZI) coated by polydimethylsiloxane (PDMS) film is proposed and investigated. The MZI with a compact size of 2.28 mm is fabricated by embedding a tapered single-mode fiber (SMF) between two multimode fibers (MMFs). Since PDMS has a higher thermo-optical coefficient than silica, the proposed sensor has better temperature sensing performance than the case without PDMS coating, which is demonstrated by simulation and experiment. The experimental results show that the sensitivity of the proposed sensor is as high as −1.06 nm/°C in the range from −5 °C to 45 °C. Full article

In complex electromagnetic environments, such as substations, converter stations in power systems, and the compartments of aircraft, trains, and automobiles, electromagnetic immunity testing is crucial. It requires that the electric field sensor has features such as a large dynamic measurement range (amplitude from hundreds of V/m to tens of kV/m), a fast response speed (response time in the order of nanoseconds or sub-nanoseconds), a wide test bandwidth (DC to 1 GHz even above), miniaturization, and robustness to strong electromagnetic interference. This paper introduces a multi-channel, ultra-wideband transient electric field measurement system. The system’s analog bandwidth covers the spectrum from DC and a power frequency of 50 Hz to partial discharge signals, from DC to 1.65 GHz, with a storage depth of 2 GB (expandable). It overcomes issues related to the instability, insufficient bandwidth, and lack of accuracy of optical fibers in analog signal transmission by using front-end digital sampling based on field-programmable gate array (FPGA) technology and transmitting digital signals via optical fibers. This approach is effectively applicable to measurements in strong electromagnetic environments. Additionally, the system can simultaneously access four channels of signals, with synchronization timing reaching 300 picoseconds, can be connected to voltage and current sensors simultaneously, and the front-end sensor can be flexibly replaced. The performance of the system is verified by means of a disconnect switch operation and steady state test in an HVDC converter station. It is effectively applicable in scenarios such as the online monitoring of transient electromagnetic environments in high-voltage power equipment, fault diagnosis, and the precise localization of radiation sources such as partial discharge or intentional electromagnetic interference (IEMI). Full article

This paper presents a numerical and experimental investigation of stimulated Brillouin scattering (SBS) in a highly birefringent spun optical fiber. When subjected to bending, the variation in the state-of-polarization (SOP) of the pump and probe waves induces a periodic oscillation of the Brillouin gain, with a period equal to the elliptical birefringence of the fiber. The experiments were corroborated by numerical simulations, combining the coupled equations governing the SOP evolution in bent spun fibers, with a scalar SBS model valid for Brillouin optical frequency-domain analysis (BOFDA) sensors. Full article

Mid-infrared (MIR) photonic sensors are revolutionizing optical sensing by enabling precise chemical and biological detection through the interrogation of molecules’ unique vibrational modes. This review explores the core principles of MIR photonics, emphasizing the light–matter interactions within the 2–20 µm wavelength range. Additionally, it examines innovative sensor architectures, such as integrated photonic platforms and optical fibers, that enhance sensitivity, specificity, and device miniaturization. The discussion extends to groundbreaking applications in environmental monitoring, medical diagnostics, industrial processes, and security, highlighting the transformative impact of these technologies. This comprehensive overview aims to illuminate the current state-of-the-art while inspiring future developments in MIR photonic sensing. Full article

This research presents a simple and effective technique to fabricate an optical sensor for ammonia detection, leveraging emission wavelength shifts as the sensing mechanism. The sensor comprises a cellulose acetate matrix doped with Eosin-Y, which serves as the electrospinning material. Photoluminescent micro/nanofibers were successfully fabricated using electrospinning and were stimulated by a 380 nm central wavelength LED. The Eosin-Y-doped electrospun fiber membranes exhibited a red emission peak at 580 nm, allowing ammonia to be detected in the linear concentration range of 0–500 ppm. The experimental results demonstrated a high sensitivity of 8.11, with a wavelength shift sensitivity of 0.029 nm/ppm in response to ammonia concentration changes. This optical sensing method effectively mitigates the influence of fluctuations in excitation light intensity, offering improved reliability. The Eosin-Y-containing electrospun fibers show great potential as a practical sensing material for detecting ammonia gas concentrations with high precision, supporting diverse applications in medical diagnostics, environmental monitoring, and industrial processes. Full article

Deep neural networks have been widely applied to fiber optic sensor systems, where the detection of external intrusion in metro tunnels is a major challenge; thus, how to achieve the optimal balance between resource consumption and accuracy is a critical issue. To address this issue, we propose a lightweight deep learning model, the Temporal Efficient Residual Network (TEResNet), for the detection of anomalous intrusion. In contrast to the majority of two-dimensional convolutional approaches, which require a deep architecture to encompass both low- and high-frequency domains, our methodology employs temporal convolutions and a compact residual network architecture. This allows the model to incorporate lower-level features into the higher-level feature formation in subsequent layers, leveraging informative features from the lower layers, and thus reducing the number of stacked layers for generating high-level features. As a result, the model achieves a superior performance with a relatively small number of layers. Moreover, the two-dimensional feature map is reduced in size to reduce the computational burden without adding parameters. This is crucial for enabling rapid intrusion detection. Experiments were conducted in the construction environment of the Guangzhou Metro, resulting in the creation of a dataset containing 6948 signal segments, which is publicly accessible. The results demonstrate that TEResNet outperforms the existing intrusion detection methods and advanced deep learning networks, achieving an accuracy of 97.12% and an F1 score of 96.15%. With only 48,009 learnable parameters, it provides an efficient and reliable solution for intrusion detection in metro tunnels, aligning with the growing demand for lightweight and robust information processing systems. Full article

This paper introduces a singular measurement infrastructure for real-time monitoring of transmission lines, applied to a 230 kV section of the Brazilian grid. The system aimed to expand the scope of monitoring variables using new concepts of optical sensing. Thus, variables are captured not only in the electrical domain but also in the mechanical, thermal, and environmental domains through optical technologies and meteorological measurement sensors strategically positioned along the transmission line. The system relies on new features, including a high-voltage polymeric insulator instrumentalized with optical fiber sensors to measure line electrical current, conductor temperature, mechanical strain, and an electro-optical signal processing unit fed by a solar system. The correlations between the monitored variables provide more complete information about what happens in the transmission line compared to the analysis of purely electrical quantities. For instance, the Spearman coefficient of 0.9909 highlights the strong correlation between anchoring force and ambient temperature. This new way of monitoring systems opens the doors to a multivariate power system analysis. Full article

Composite materials are increasingly used in the aviation industry for various aircraft components due to their lightweight and mechanical performances. However, these materials are susceptible to degradation due to environmental factors such as hot–wet environments and freeze–thaw cycles, which can compromise their performance and safety over time. This study develops an innovative Fiber Bragg Grating (FBG) sensor system capable of not only measuring but also decoupling the simultaneous effects of temperature, humidity and strain. Unlike existing FBG systems, our approach integrates a novel theoretical framework and sensor configuration that accurately isolates these parameters in an epoxy resin material. The system incorporates three FBG sensors: one for temperature, one for temperature and humidity and a third one for all three factors. A theoretical framework based on linear strain superposition and constitutive laws was developed to isolate the individual contributions of each factor. Experimental validation in controlled hygrothermal conditions demonstrated the system’s ability to accurately detect and decouple these effects, enabling the monitoring of moisture absorption and composite degradation over time. The proposed system provides a reliable, lightweight and efficient solution for the long-term monitoring of composite structures in extreme conditions. Additionally, it enhances predictive maintenance by improving the accuracy of Health and Usage Monitoring Systems (HUMSs) and provides a method to correct data inconsistencies in already installed sensors, further extending their operational value. Full article

Optical fiber-based acoustic detectors for ultrasound imaging in medical field feature plano-concave Fabry–Perot cavities integrated on fiber tips, realized via dip-coating. This technique imposes constraints on sensor geometry, potentially limiting performance. Lab-on-Fiber technology enables complex three-dimensional structures with precise control over geometric parameters, such as the curvature radius. A careful investigation of the optical and mechanical aspects involved in the sensors’ performances is crucial for determining the design rules of such probes. In this study, we numerically analyzed the impact of curvature on the optical and acoustic properties of a plano-concave cavity using the Finite Element Method. Performance metrics, including sensitivity, bandwidth, and directivity, were compared to planar Fabry–Perot configurations. The results suggest that introducing curvature significantly enhances sensitivity by improving light confinement, especially for cavity thicknesses exceeding half the Rayleigh zone (∼45 μm), reaching an enhancement of 2.5 a L = 60 μm compared to planar designs. The curved structure maintains high spectral quality (FOM) despite 2% fabrication perturbations. A mechanical analysis confirms no disadvantages in acoustic response and bandwidth (∼40 MHz). These findings establish curved plano-concave structures as robust and reliable for high-sensitivity polymeric lab-on-fiber ultrasound detectors, offering improved performance and fabrication tolerance for MHz-scale bandwidth applications. Full article

A positioning method for a roadheader based on fiber-optic strap-down inertial navigation and binocular vision is proposed to address the issue of low measurement accuracy of the mining machine position caused by single-sensor methods in underground coal mines. A vision system for the mining machine position is constructed based on the four-point target fixed on the body of the roadheader, and the position and attitude information of the roadheader are obtained by combining the inertial navigation on the body. To deal with the problem of position detection inaccuracies caused by the accumulation of errors in inertial navigation measurements over time and disturbances from body vibrations to the combined positioning system, an Adaptive Derivative Unscented Kalman Filtering (ADUKF) algorithm is proposed, which can suppress the impact of process variance uncertainties on the filtering. The simulation results demonstrate that, compared to the Unscented Kalman Filtering algorithm, the position errors in the three directions are reduced by 20%, 20.68%, and 28.57%, respectively. Experiments demonstrate that the method can compensate for the limitations of single-measurement methods and meet the positioning accuracy requirements for underground mining standards. Full article

Demodulation of fiber optic Fabry–Pérot (F-P) acoustic sensors with high sensitivity and a large dynamic range continues to pose significant challenges. In this paper, we propose an advanced phase-generated carrier (PGC) demodulation algorithm, applied innovatively to membrane-free F-P acoustic sensors operating under high sound pressure. The algorithm optimizes acoustic demodulation results by adjusting the mixing phase delay, achieving the best signal to noise and distortion ratio (SINAD) and total harmonic distortion (THD) (<1%). Additionally, by introducing the cosine component of the acoustic signal obtained directly after filtering the interference signal, into the demodulation algorithm process, the sensitivity of the sensor at high sound pressure is significantly improved. The experimental results show that the ameliorated algorithm obtains a demodulation sensitivity of $34.95\mathsf{\mu }$rad/Pa and a THD of 0.87%, both of which are superior to traditional PGC demodulation algorithms under the same experimental conditions. At the same time, the minimum detectable sound pressure of $129.73{\mathrm{mPa/Hz}}^{1/2}$ was obtained, and the sound pressure tested in the experiment at a frequency of 1 kHz was as high as 3169.78 Pa (164 dB). With the proposed algorithm, the flatness of the frequency response is ±0.82 dB from 100 Hz to 33 kHz, and a dynamic range of up to 102.6 dB was obtained, making it relevant in the field of aerospace acoustic measurements. Full article