Search Results (74)

In oil well environments, pressure sensors are often challenged by electromagnetic interference, temperature drift, and corrosive fluids, which reduce their stability and service life. To improve long-term reliability under these conditions, we developed a fiber optic Fabry–Perot (FP) cavity pressure sensor that employs an Inconel 718 diaphragm to provide both high mechanical strength and corrosion resistance. An integrated fiber Bragg grating (FBG) was included to monitor temperature simultaneously, allowing temperature–pressure cross-sensitivity to be decoupled. The sensor was fabricated and tested over a temperature range of 20–100 °C and a pressure range of 0–60 MPa. Experimental characterization showed that the FP cavity length shifted linearly with pressure, with a sensitivity of 377 nm/MPa, while the FBG demonstrated a temperature sensitivity of 0.012 nm/°C. After temperature compensation, the overall pressure measurement accuracy reached 0.5% of the full operating pressure range (0–60 MPa). These results confirm that the combined FP–FBG sensing approach maintained stable performance in harsh downhole conditions, making it suitable for pressure monitoring in shallow and medium-depth reservoirs. The proposed design offers a practical route to extend the operational lifetime of optical sensors in oilfield applications. Full article

In this research, precision temperature sensing for electromagnetically harsh environments was achieved utilizing a low-threshold frequency-stable optoelectronic oscillator (OEO) leveraging stimulated Brillouin scattering (SBS). The sensing mechanism relied on the temperature-dependent frequency shift in the SBS-induced notch filter. By embedding this filter in the OEO feedback loop, the oscillator’s output frequency was locked to the difference between the optical carrier frequency and the SBS notch center frequency. The temperature variations were translated into microwave frequency shifts through OEO oscillation, which was quantified with heterodyne detection. To suppress environmental perturbations, a Faraday rotation mirror (FRM) was integrated at the fiber end, creating a dual-pass SBS interaction that simultaneously enhanced the vibration immunity and reduced the SBS power threshold by 2.7 dB. The experimental results demonstrated a sensitivity of 1.0609 MHz/°C (R² = 0.999) and a long-term stability of ±0.004 °C. This innovative scheme demonstrated significant advantages over conventional SBS-OEO temperature sensing approaches, particularly in terms of threshold reduction and environmental stability enhancement. Full article

Fourier transform-based frequency sweeping interferometry (FT-FSI) is an interferometric technique that enables absolute distance measurement by detecting the beat frequencies from the interference of reflected signals. This method allows robust, simultaneous distance measurements to multiple targets and is largely immune to variations in the reflected optical signal intensity. As a result, FT-FSI maintains accuracy even when measuring reflectors with low reflectance. FT-FSI has recently been integrated into the full remote alignment system (FRAS) developed for the High-Luminosity Large Hadron Collider (HL-LHC) project at CERN. Designed to operate in harsh environments with electromagnetic interference, ionizing radiation and cryogenic temperatures, FRAS employs FT-FSI for the precise monitoring of the alignment of accelerator components. The system includes specialized interferometers and a range of sensors, including inclinometers, distance sensors, and leveling sensors. This paper presents a comprehensive review of the challenges associated with remote measurement and monitoring systems in harsh environments such as those of particle accelerators. It details the development and validation of the FT-FSI-based measurement system, emphasizing its critical role in enabling micrometric alignment accuracy. The developments and results presented in this work can be readily translated to other demanding metrology applications in harsh environments. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

An optical fiber force sensor based on the Vernier effect in cascaded Fabry–Perot interferometers (FPIs) formed by a barium tantalate microsphere and a section of polymethyl methacrylate (PMMA) optical fiber is proposed and investigated. Optical fiber sensors offer numerous advantages over their electronic counterparts, including immunity to electromagnetic interference and suitability for harsh environments. Despite these benefits, current optical fiber force sensors often face limitations in sensitivity, reliability, and fabrication costs. The proposed sensor has the potential to address these issues. Simulations and experimental results demonstrate that the sensor achieves a sensitivity of 9279.66 nm/N in a range of up to 3 mN. The sensor also exhibits excellent repeatability, making it a promising candidate for high-performance force monitoring in various challenging environments. Full article

With the expansion of urban areas and the increase in the number of vehicles, the complexity and harshness of the electromagnetic environment for vehicular communication in cities have significantly intensified. Traditional methods for evaluating the electromagnetic compatibility (EMC) of vehicular communication systems face substantial limitations. With the advancement of high-resolution satellite remote sensing image technology, the acquisition of high-precision urban models has become more accessible, significantly enhancing applications in the field of communication systems. Therefore, a novel EMC evaluation method for vehicular wireless communication systems based on urban high-resolution satellite remote sensing images is proposed in this paper. By analyzing the characteristics of such systems and integrating the requirements of practical urban communication scenarios and vehicular tasks, EMC evaluation indicators were selected, and a hierarchical evaluation indicators system was constructed, comprising target, criterion, and sub-criterion layers. The proposed method leverages the strengths of TOPSIS, AHP, and FCE methods, utilizing quantitative TOPSIS and qualitative AHP to determine the weights of the criterion and sub-criterion layers, respectively. The FCE method was employed to evaluate the EMC of the vehicular wireless communication system. The rationality and feasibility of the method were validated through practical communication experiments conducted with a vehicle in an urban environment. Full article

Resonators are passive components that respond to an excitation signal by oscillating at their natural frequency with an exponentially decreasing amplitude. When combined with antennas, resonators enable purely passive chipless sensors that can be read wirelessly. In this contribution, we investigate the properties of dielectric resonators, which combine the following functionalities: They store the readout signal for a sufficiently long time and couple to free space electromagnetic waves to act as antennas. Their mode spectrum, along with their resonant frequencies, quality factor, and coupling to electromagnetic waves, is investigated using a commercial finite element program. The fundamental mode exhibits a too-low overall Q factor. However, some higher modes feature overall Q factors of several thousand, which allows them to act as transponders operating without integrated circuits, batteries, or antennas. To experimentally verify the simulations, isolated dielectric resonators exhibiting modes with similarly high radiation-induced and dissipative quality factors were placed on a low-loss, low permittivity ceramic holder, allowing their far-field radiation properties to be measured. The radiation patterns investigated in the laboratory and outdoors agree well with the simulations. The resulting radiation patterns show a directivity of approximately 7.5 dBi at 2.5 GHz. The sensor was then heated in a ceramic furnace with the readout antenna located outside at room temperature. Wireless temperature measurements up to 700 °C with a resolution of 0.5 °C from a distance of 1 m demonstrated the performance of dielectric resonators for practical applications. Full article

Wireless power transfer (WPT) is recognized as a critical technology to advance carbon neutrality in transportation by alleviating charging challenges for electric vehicles and accelerating their adoption to replace fossil fuel. To ensure durability under traffic loads and harsh environments while avoiding vehicle obstructions, WPT primary circuits should be embedded within pavement structures rather than surface-mounted. This study systematically investigated the optimization of magnetite-modified asphalt material composition and thickness for enhancing electromagnetic coupling in WPT systems through integrated numerical and experimental approaches. A 3D finite element model (FEM) and a WPT platform with primary-side inductor–capacitor–capacitor (LCC) and secondary-side series (S) compensation were developed to assess the electromagnetic performance of magnetite content ranging from 0 to 25% and pavement thickness ranging from 30 to 70 mm. Results indicate that magnetite incorporation increased efficiency from 80.3 to 84.7% and coupling coefficients from 0.236 to 0.242, with power loss increasing by only 0.25 W. This enhancement is driven by improved equivalent permeability, which directly enhances magnetic coupling efficiency. A critical pavement thickness of 50 mm was identified, beyond which the reduction in transmission efficiency increased significantly due to magnetic flux dispersion. Additionally, the nonlinear increase in power loss is partially attributed to the significant rise in hysteresis and eddy current losses at elevated magnetite content levels. The proposed design framework, which focuses on 10% magnetite content and a total pavement thickness of 50 mm, achieves an optimal energy transfer efficiency. This approach contributes to sustainable infrastructure development for wireless charging applications. Full article

Acoustic sensing has many applications in engineering, one of which is fiber-optic hydrophones (FOHs). Conventional piezoelectric hydrophones face limitations related to size, electromagnetic interference, corrosion, and narrow operating bandwidth. Fiber-optic hydrophones, particularly those employing distributed feedback (DFB) lasers, offer a compelling alternative due to their mechanical flexibility, resistance to harsh conditions, and broad detection range. DFB lasers are highly sensitive to external perturbations such as temperature and strain, enabling the precise detection of underwater acoustic signals by monitoring the resultant shifts in lasing wavelength. This paper presents an enhanced interrogation mechanism that leverages Mach–Zehnder interferometers to translate wavelength shifts into measurable phase deviations, thereby providing cost-effective and high-resolution phase-based measurements. A dual interferometric setup is integrated with a standard demodulation algorithm to extend the dynamic range of these sensing systems. The experimental results demonstrate a substantial improvement in performance, with the dynamic range increasing from 125 dB to 139 dB at 1 kHz without degrading the noise floor. This enhancement significantly expands the utility of FOH-based systems in underwater environments, supporting applications such as underwater surveillance, submarine communication, and marine ecosystem monitoring. Full article

Adapting to extremely harsh service environments is an unavoidable challenge for microwave absorption materials. In this paper, C/PyC/SiC composites were prepared by a vacuum impregnation, curing and cracking process with various preparation cycles, and the stress–strain curves were further discussed. The results show that after three cycles, the C/PyC/SiC composite showed significantly enhanced mechanical properties of 83.59 MPa at around 35.00% strain, and it also possessed the best overall electromagnetic microwave absorption performance, with a minimum reflection loss value of −46.04 dB at 16.06 GHz and 1.90 mm of thickness. All in all, we have introduced an innovative method for fabricating electromagnetic microwave-absorbing materials capable of withstanding harsh environmental conditions. Full article

Wireless passive temperature sensors are essential in environments where wired connections are impractical, such as rotating machinery and harsh conditions. A key advantage of these sensors is their ability to operate without a local power source. This study employs the antenna backscattering method, which relies on the wireless interaction between the interrogator antenna and the sensor antenna’s resonant frequency, implemented in the far-field region to support long communication distances. To evaluate the impact of antenna shape on sensor performance, three microstrip patch antenna shapes—rectangular, circular, and equilateral triangular—were designed to operate in the fundamental mode at 2.4 GHz. These designs were simulated using HFSS in Ansys Electromagnetic Suite^® 2023 R1 (Ansys Inc., Canonsburg, PA, USA), fabricated on alumina substrates, and assessed for performance metrics, including communication distance and sensitivity. Results indicated that the equilateral triangular patch outperformed the others, achieving a maximum communication distance of 16.5 cm, a sensitivity of 0.129 MHz/°C over a temperature range of 25 °C to 500 °C, and a simulated gain of 5.84 dBi. These findings underscore the importance of antenna shape selection and optimization for robust, wireless temperature sensing in demanding environments. Full article

RFID technology has been widely researched and used for structural health applications because of its compact, wireless, and scalable nature. This technology is divided into chipped and chipless sensors. Chipped sensors are costly due to their chipped tags, have narrowband operations, and contribute to shortcomings in detection capability. Chipless tags provide real-time monitoring of cracks in harsh environments like high-temperature areas and high electromagnetic interference areas. This paper presents a design of a novel chipless hybrid circular-hexagon sensor that uses the frequency signature-based method for metal crack detection and characterization using wideband frequency. This sensor is small in size (16 mm × 16 mm × 1.4 mm) and easily mountable in hard-to-reach areas. It is a low-cost, passive chipless sensor that can wirelessly monitor the cracks in metallic structures. The radar cross-section of the chipless tag shows a shift in the resonant frequency of the tag under crack and no crack conditions. Key contributions of this work are that through simulations and experimental investigation, the tag is shown to be able to detect mm-scale cracks, validating the concept and correlating the presence and size of the cracks based on the shift in resonant frequencies in which a pair of Vivaldi antennas are used as a transmitter and receiver to connect to the VNA. The designed small sensor tag is tested in a benchtop setup with no prior calibration, imitating the real-time environment conditions for crack detection. Full article

The accurate monitoring of the internal hotspot temperature in transformers is crucial for ensuring the stability of power grid operations. Traditional methods typically measure only the surface temperature of transformers, whereas this study proposes a non-invasive thermal inversion algorithm based on a wireless mesh network that effectively predicts the internal hotspot temperature. An electromagnetic-thermal-fluid coupling simulation model was developed to simulate the temperature distribution in transformers under various operating conditions. Subsequently, this study employed a Support Vector Regression (SVR) algorithm to train the sample dataset, optimizing the SVR model using a grid search and cross-validation to enhance the predictive accuracy. After training, the model estimates the hotspot temperature based on surface measurements obtained through a non-contact infrared sensor network. The wireless mesh network, based on the Wi-Fi protocol, provides robust and real-time monitoring even in harsh environments, with data transmitted to a central root node via multiple sensor nodes. The experimental results demonstrate that this method is highly accurate, with predicted temperatures closely matching the results from traditional measurement techniques. This method enhances transformer condition monitoring, helping to extend the transformer lifespan and improve power grid stability. Full article

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time–frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time–frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%. Full article

Inductive proximity sensors are relevant in position-sensing applications in many industries but, in order to be used in harsh industrial environments, they need to be immune to electromagnetic interference (EMI). The use of conventional filters to mitigate these perturbations often compromises signal bandwidth, ranging from 100 Hz to 1.6 kHz. We have exploited recent advances in the field of artificial intelligence (AI) to study the ability of neural networks (NNs) to automatically filter out EMI features. This study offers an analysis and comparison of possible NN models (a 1D convolutional NN, a recurrent NN, and a hybrid convolutional and recurrent approach) for denoising EMI-perturbed signals and proposes a final model, which is based on gated recurrent unit (GRU) layers. This network is compressed and optimised to meet memory requirements, so that in future developments it could be implemented in application-specific integrated circuits (ASICs) for inductive sensors. The final RNN manages to reduce noise by 70% (MSE_red) while occupying 2 KB of memory. Full article