AI Sensors and Transducers

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI Sensors aisens	-	-	2025	15.0 days *	CHF 1000
Chemosensors chemosensors	4.4	8.1	2013	19.8 Days	CHF 2000
Micromachines micromachines	3.5	7.1	2010	16.6 Days	CHF 2100
Robotics robotics	3.6	7.4	2012	20 Days	CHF 1800
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600

20 pages, 4671 KB

Open AccessArticle

An Improved IHBA-BP Neural Network for Temperature Compensation of Load Cells

by Zhen-Jie Zhang, Wan-Sheng Cheng and Dai-Xing Zhang

Sensors 2026, 26(12), 3691; https://doi.org/10.3390/s26123691 - 10 Jun 2026

Viewed by 349

Temperature variations degrade load cell accuracy. To address this problem, an Improved Honey Badger Algorithm (IHBA) was developed to optimize the weights and biases of a BP neural network. IHBA incorporates uniform initialization, a nonlinear weight factor, and Lévy flight with stagnation-aware triggering [...] Read more.

Temperature variations degrade load cell accuracy. To address this problem, an Improved Honey Badger Algorithm (IHBA) was developed to optimize the weights and biases of a BP neural network. IHBA incorporates uniform initialization, a nonlinear weight factor, and Lévy flight with stagnation-aware triggering to overcome the uneven initialization, poor exploration–exploitation balance, and weak local optima escape capability of the standard HBA. To validate the proposed method, a dedicated calibration experimental system was constructed. A 7075 T6 aluminum load cell (50 kN) was tested under 0–50 kN loading–unloading cycles over a temperature range of 0–60 °C. To eliminate random errors, three identical elastomers were fabricated, each tested three times, and the measured values were averaged. The results show that after IHBA-BP compensation, the zero-temperature drift coefficient of the load cell was reduced from 374.8 ppm/°C to 35.09 ppm/°C, and the sensitivity-temperature coefficient was reduced from 936.94 ppm/°C to 45.75 ppm/°C. On the unseen test set, the relative error after compensation was 0.01207, the mean square error was 2.84 × 10⁻⁵, and the root mean square error was 0.00533. Compared with IMA-BP, PSO-BP, BP, and polynomial fitting methods, IHBA-BP achieved the lowest error. The proposed method shows strong potential for industrial load cell temperature compensation. Full article

(This article belongs to the Topic AI Sensors and Transducers)

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30 pages, 6991 KB

Open AccessArticle

Protection-Oriented Non-Intrusive Arc Fault Detection in Photovoltaic DC Systems via Rule–AI Fusion

by Lu HongMing and Ko JaeHa

Sensors 2026, 26(10), 3138; https://doi.org/10.3390/s26103138 - 15 May 2026

Viewed by 531

Abstract

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and [...] Read more.

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

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13 pages, 945 KB

Open AccessArticle

Application of Smart Sensors in Commodity Management

by Chao-Kong Chung, Meng-Yun Chung and Guo-Ming Sung

Sensors 2026, 26(10), 3096; https://doi.org/10.3390/s26103096 - 14 May 2026

Viewed by 431

Abstract

Integrating sensors with wireless communication capabilities into smart wireless sensing devices allows us to form a wireless sensing network. This network works in conjunction with monitors to display and control parameters at different locations or in the environment. By deploying a wireless sensing [...] Read more.

Integrating sensors with wireless communication capabilities into smart wireless sensing devices allows us to form a wireless sensing network. This network works in conjunction with monitors to display and control parameters at different locations or in the environment. By deploying a wireless sensing network, the system can interact with the user by sending notifications when necessary, based on the environmental conditions and user activities detected by the wireless sensors, and make corresponding adjustments to or control the environment. The advancement and widespread adoption of the internet have enabled the development of this technology. Wireless sensors are widely used in product positioning and environmental monitoring management, making the management of complex products more accurate. The Monitor and Control System (MCS), which combines network cameras and wireless sensors with neural network technology and fuzzy control systems, improves the existing positioning method and enhances positioning accuracy. Product management, which comprises comprehensive digital services and is facing serious staff shortages, has turned to digital payment to reduce labor costs. This experiment was simulated using Network Simulator 2 (NS2). In the sensing system part, the application of a ZigBee network and its status were explored, and interference was analyzed. Information on network interference simulations and their impact on normal services was compiled for network management purposes. Using NS2 network simulation, this study utilizes ZigBee with different neuron nodes and different training times to find the best network model, compares various queuing mechanisms and functions as a network interference intrusion detection system, and explores its node defense capabilities in cases of interference. Node Density: Node density is typically determined by the number of nodes in the simulation area and the size of the scene. Low Density: Sparse node distribution, prone to network partitioning, is suitable for testing latency-tolerant networks (DTNs) or route discovery capabilities. High Density: It entails dense node distribution, severe signal interference, and packet collisions. It is suitable for testing MAC layer collision prevention mechanisms (such as CSMA/CA) and the scalability of outing protocols. Configuration Method: the “set Dest” tool is used in a Tcl script to generate a mobile scene file, defining the number of nodes, range (X, Y), and time to be more significant in product management. Full article

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13 pages, 3720 KB

Open AccessArticle

Flexible High-Frequency Underwater Transducer Based on Piezoelectric Composites

by He Zhou, Chao Zhong and Lei Qin

Micromachines 2026, 17(5), 577; https://doi.org/10.3390/mi17050577 - 7 May 2026

Viewed by 601

Abstract

In this study, a flexible, lightweight high-frequency underwater transducer (FT) was designed and fabricated. To ensure the flexibility and reliability of the transducer, a flexible piezoelectric composite material with a same-side electrode configuration and a perforated flexible printed circuit (FPC) were designed. First, [...] Read more.

In this study, a flexible, lightweight high-frequency underwater transducer (FT) was designed and fabricated. To ensure the flexibility and reliability of the transducer, a flexible piezoelectric composite material with a same-side electrode configuration and a perforated flexible printed circuit (FPC) were designed. First, finite element simulation analysis was performed on the flexible piezoelectric composites to optimize the structural parameters. Next, using a cutting and infusion method combined with reflow soldering, the piezoelectric composites and FPC were integrated to form a flexible sensing element. Finally, a flexible packaging process for the underwater transducer was investigated, resulting in a flexible underwater transducer with a thickness of only 5.3 mm. The results of underwater electroacoustic comparison tests show that the transmission and reception performance of the FT differs by less than 0.5 dB from that of conventional rigid transducers. This demonstrates that the flexible underwater transducer developed in this study not only possesses the advantages of flexibility and can be conformally mounted on curved surfaces but also achieves acoustic performance comparable to that of traditional rigid transducers, thereby providing new insights for the lightweight development of underwater transducers. Full article

(This article belongs to the Topic AI Sensors and Transducers)

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28 pages, 12735 KB

Open AccessArticle

FMW-YOLO: A Frequency-Enhanced and Multi-Scale Context-Aware Framework for PCB Defect Detection

by Yuguo Li, Shuo Tian, Wenzheng Sun, Longfa Chen, Jian Li, Junkai Hu and Na Meng

Micromachines 2026, 17(5), 531; https://doi.org/10.3390/mi17050531 - 27 Apr 2026

Cited by 4 | Viewed by 909

Abstract

A high-precision and efficient surface defect detection for printed circuit board (PCB) is critical to ensuring the reliability of electronic systems. However, the presence of complex circuit backgrounds and the small scale of defects often limit the precision and effectiveness of conventional inspection [...] Read more.

A high-precision and efficient surface defect detection for printed circuit board (PCB) is critical to ensuring the reliability of electronic systems. However, the presence of complex circuit backgrounds and the small scale of defects often limit the precision and effectiveness of conventional inspection approaches. To address these challenges, this paper proposes FMW-YOLO, a lightweight and accurate detection framework based on YOLO11n. Specifically, a Frequency-Enhanced Channel-Transposed and Local Feature backbone network is developed to improve feature extraction. By designing a Dual-Frequency and Channel Attention Aggregation module and a Lightweight Edge-Gaussian Block, the original C3k2 structure is refined to suppress noise interference while preserving high-frequency details, thereby enhancing feature representation. Furthermore, a neck network incorporating a Multi-Scale Context-Aware Enhancement mechanism is constructed, in which an Attention-Integrated Feature Pyramid is employed to facilitate more effective cross-scale feature interaction. In addition, a Dilated Reparam Residual Module is embedded into the C3k2 structure to expand the receptive field without significantly increasing computational burden. Finally, Wise-IoU is adopted to optimize bounding box regression by assigning greater importance to anchors of moderate quality. Extensive experiments conducted on the HRIPCB and DeepPCB datasets demonstrate that FMW-YOLO improves mAP50 by 2.1% and 0.3%, respectively, while reducing the number of parameters by 23%. These results indicate that the proposed method achieves improved detection accuracy and demonstrates strong potential for practical industrial applications. Full article

(This article belongs to the Topic AI Sensors and Transducers)

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20 pages, 3255 KB

Open AccessArticle

Seamless Indoor and Outdoor Navigation Using IMU-GNSS Sensor Data Fusion

by Bismark Kweku Asiedu Asante and Hiroki Imamura

Sensors 2026, 26(7), 2215; https://doi.org/10.3390/s26072215 - 3 Apr 2026

Viewed by 1233

Abstract

Seamless localization across indoor and outdoor environments remains a fundamental challenge for wearable navigation systems, particularly those intended to assist visually impaired individuals. This challenge arises from the unreliability of GNSS signals in indoor and transitional spaces and the cumulative drift inherent to [...] Read more.

Seamless localization across indoor and outdoor environments remains a fundamental challenge for wearable navigation systems, particularly those intended to assist visually impaired individuals. This challenge arises from the unreliability of GNSS signals in indoor and transitional spaces and the cumulative drift inherent to IMU–based dead reckoning. To address these limitations, this paper proposes a physics-informed GNSS–IMU sensor fusion framework that enables robust, real-time wearable navigation across heterogeneous environments. The proposed system dynamically adapts to environmental context, employing GNSS dominant localization in outdoor settings and PINN enhanced IMU-based dead reckoning during GNSS denied indoor operation. At the core of the framework is a tightly coupled Physics-Informed Neural Network (PINN) and Extended Kalman Filter (EKF), where the PINN embeds kinematic motion constraints to correct inertial drift and suppress sensor noise, while the EKF performs probabilistic state estimation and sensor fusion. The framework is implemented on a compact, energy-efficient wearable platform and evaluated using real-world indoor–outdoor pedestrian trajectories. Experimental results demonstrate improved localization accuracy, significantly reduced drift during indoor navigation, and stable indoor–outdoor transitions compared to conventional GNSS–IMU fusion methods. The proposed approach offers a practical and reliable solution for wearable assistive navigation and has broader applicability in smart mobility and autonomous wearable systems. Full article

(This article belongs to the Topic AI Sensors and Transducers)

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18 pages, 12307 KB

Open AccessArticle

Investigation of a Novel Piezoelectric Harvester for Capturing Rotational Motion

by Junxiang Jiang, Heming Wang and Liang Wang

Micromachines 2026, 17(2), 255; https://doi.org/10.3390/mi17020255 - 16 Feb 2026

Viewed by 1758

Abstract

Piezoelectric energy harvesting technology has received great research interest in recent years. To harvest energy from rotational motion, this work proposes a cantilevered piezoelectric energy harvester based on an adjustable rigid parallel connection. The baffle was designed as a carrier for the rigid [...] Read more.

Piezoelectric energy harvesting technology has received great research interest in recent years. To harvest energy from rotational motion, this work proposes a cantilevered piezoelectric energy harvester based on an adjustable rigid parallel connection. The baffle was designed as a carrier for the rigid connection of the piezoelectric beams A, B and C. The theoretical model of the device was established, and equations for voltage and power were derived. The calculated intrinsic frequencies of the piezoelectric beams are consistent with the experimental results. The baffle size, the distance from the baffle to the free end, and the number of rotor bumps were used as variables in the experiments. The experimental results show that the proposed piezoelectric energy harvester can harvest energy across multiple frequency bands. The maximum average power of the proposed piezoelectric energy harvester is 110.49 mW at a load resistance of 10 kΩ and a rotational speed of 240 r/min. The maximum average power of the harvester is 36.44 mW at a load resistance of 10 kΩ and a rotational speed of 60 r/min. The rigid parallel connection not only broadens the energy harvesting bandwidth but also enhances the output performance of the harvester. Full article

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22 pages, 2447 KB

Open AccessArticle

Word-Level Motion Learning for Contactless QWERTY Typing with a Single Camera

by Sung-Sic Yoo and Heung-Shik Lee

Sensors 2026, 26(4), 1087; https://doi.org/10.3390/s26041087 - 7 Feb 2026

Viewed by 647

Abstract

Contactless text entry is increasingly important in immersive and constrained computing environments, yet most vision-based approaches rely on character-level recognition or key localization, which are fragile under monocular sensing. This study investigates the feasibility of recognizing natural QWERTY typing motions directly at the [...] Read more.

Contactless text entry is increasingly important in immersive and constrained computing environments, yet most vision-based approaches rely on character-level recognition or key localization, which are fragile under monocular sensing. This study investigates the feasibility of recognizing natural QWERTY typing motions directly at the word level using only a single RGB camera, under a fixed single-user and single-camera configuration. We propose a word-level contactless typing framework that models each word as a distinctive spatiotemporal finger motion pattern derived from hand joint trajectories. Typing motions are temporally segmented, and direction-aware finger displacements are accumulated to construct compact motion representations that are relatively insensitive to absolute hand position and typing duration within the evaluated setup. Each word is represented by multiple motion prototypes that are incrementally updated through online learning with a trial-delayed adaptation protocol. Experiments with vocabularies of up to 200 words show that the proposed approach progressively learns and recalls word-level motion patterns through repeated interaction, achieving stable recognition performance within the tested configuration at realistic typing speeds. Additional evaluations demonstrate that learned motion representations can transfer from physical keyboards to flat-surface typing within the same experimental setting, even when tactile feedback and visual layout cues are reduced. These results support the feasibility of reframing contactless typing as a word-level motion recall problem, and suggest its potential role as a complementary component to character-centric camera-based input methods under constrained monocular sensing. Full article

(This article belongs to the Topic AI Sensors and Transducers)

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19 pages, 3069 KB

Open AccessArticle

Ab Initio Studies of Work Function Changes Induced by Single and Co-Adsorption of NO, CO, CO₂, NO₂, H₂S, and O₃ on ZnGa₂O₄(111) Surface for Gas Sensor Applications

by Jen-Chuan Tung, Guan-Yu Chen, Chao-Cheng Shen and Po-Liang Liu

Sensors 2026, 26(2), 415; https://doi.org/10.3390/s26020415 - 8 Jan 2026

Cited by 1 | Viewed by 989

Abstract

In this study, first-principles density functional theory (DFT) calculations were employed to investigate the effects of single and binary gas adsorption of NO, CO, CO₂, NO₂, H₂S, and O₃ on the ZnGa₂O₄(111) [...] Read more.

In this study, first-principles density functional theory (DFT) calculations were employed to investigate the effects of single and binary gas adsorption of NO, CO, CO₂, NO₂, H₂S, and O₃ on the ZnGa₂O₄(111) surface. For single-gas adsorption, O₃ adsorbed on surface Ga sites induces a pronounced work-function increase of 0.97 eV, whereas H₂S adsorption at surface O sites yields the strongest adsorption energy (−1.21 eV), highlighting their distinct electronic interactions with the surface. For binary co-adsorption, the NO₂-O₃ pair adsorbed at Ga-coordinated sites produces the largest work-function shift (1.88 eV), while adsorption at Zn sites results in the most stable configuration, with an adsorption energy reaching −3.98 eV. These results indicate that co-adsorption of highly electronegative gases can significantly enhance charge transfer and sensing response. In contrast, mixed oxidizing–reducing gas pairs, such as NO₂-H₂S, lead to a markedly suppressed work-function variation (−0.02 eV), suggesting reduced sensor sensitivity due to compensating charge-transfer effects. Overall, this work demonstrates that gas-sensing behavior on ZnGa₂O₄(111) is governed not only by individual gas–surface interactions but also by cooperative and competitive effects arising from binary co-adsorption, providing insights into realistic multi-gas sensing environments. Full article

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23 pages, 4295 KB

Open AccessArticle

Scene Understanding System of Underground Pipeline Corridors Under Characteristic Degradation Conditions

by Jing Wang, Ruiyao Xing, Meng Zhou, Jingbang Xu, Xiaoping Zhang and Shuang Ju

Sensors 2026, 26(1), 141; https://doi.org/10.3390/s26010141 - 25 Dec 2025

Viewed by 674

Abstract

Accurate scene understanding is crucial for the safe and stable operation of underground utility tunnel inspections. Addressing the characteristics of low-light environments, this paper proposes an object recognition method based on low-light enhanced image semantic segmentation. Secondly, by analyzing image data from real [...] Read more.

Accurate scene understanding is crucial for the safe and stable operation of underground utility tunnel inspections. Addressing the characteristics of low-light environments, this paper proposes an object recognition method based on low-light enhanced image semantic segmentation. Secondly, by analyzing image data from real underground utility tunnel environments, the visual language model undergoes scene image fine-tuning to generate scene description text. Thirdly, integrating these functionalities into the system enables real-time processing of captured images and generation of scene understanding results. In practical applications, the average accuracy of the improved recognition model increased by nearly 1% compared to the original model, while the accuracy and recall of the fine-tuned visual-language model surpassed the untuned model by over 70%. Full article

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23 pages, 4147 KB

Open AccessArticle

GCEA-YOLO: An Enhanced YOLOv11-Based Network for Smoking Behavior Detection in Oilfield Operation Areas

by Qing Liu, Xiaojing Wan, Yuzhou Sheng, Shuo Wang and Bo Wei

Sensors 2026, 26(1), 103; https://doi.org/10.3390/s26010103 - 23 Dec 2025

Cited by 1 | Viewed by 1688

Abstract

Smoking in oilfield operation areas poses a severe risk of fire and explosion accidents, threatening production safety, workers’ lives, and the surrounding ecological environment. Such behavior represents a typical preventable unsafe human action. Detecting smoking behaviors among oilfield workers can fundamentally prevent such [...] Read more.

Smoking in oilfield operation areas poses a severe risk of fire and explosion accidents, threatening production safety, workers’ lives, and the surrounding ecological environment. Such behavior represents a typical preventable unsafe human action. Detecting smoking behaviors among oilfield workers can fundamentally prevent such safety incidents. To address the challenges of low detection accuracy for small objects and frequent missed or false detections under extreme industrial environments, this paper proposes a GCEA-YOLO network based on YOLOv11 for smoking behavior detection. First, a CSP-EDLAN module is introduced to enhance fine-grained feature learning. Second, to reduce model complexity while preserving critical spatial information, an ADown module is incorporated. Third, an enhanced feature fusion module is integrated to achieve effective multiscale feature aggregation. Finally, an EfficientHead module is employed to generate high-precision and lightweight detection results. The experimental results demonstrate that, compared with YOLOv11n, GCEA-YOLO achieves improvements of 20.8% in precision, 6.9% in recall, and 15.1% in mean average precision (mAP). Overall, GCEA-YOLO significantly outperforms YOLOv11n. Full article

(This article belongs to the Topic AI Sensors and Transducers)

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33 pages, 6643 KB

Open AccessArticle

Smart Water Management: An Energetically Autonomous IoT-Based Application for Pressure and Flow Monitoring in Water Distribution Systems

by Jonatha B. Silva, Lucas D. de Oliveira, Rafael M. Duarte, Cícero de Rocha Souto and Juan M. M. Villanueva

Sensors 2025, 25(23), 7227; https://doi.org/10.3390/s25237227 - 26 Nov 2025

Cited by 3 | Viewed by 3022

Abstract

The distribution of water in urban areas involves several challenges, such as maintaining pipelines, controlling pressure and flow, and monitoring water quality. In particular, the measurement of the flow rate and pressure in pipelines is essential for optimizing water distribution in cities. In [...] Read more.

The distribution of water in urban areas involves several challenges, such as maintaining pipelines, controlling pressure and flow, and monitoring water quality. In particular, the measurement of the flow rate and pressure in pipelines is essential for optimizing water distribution in cities. In recent decades, new technologies have been used to address these challenges, such as hydraulic modeling systems with software, smart sensors, and automated control systems. Among the new possibilities, the use of wireless sensor networks has been highlighted. In this sense, IoT-based nodes have been proposed as a low-cost alternative, with the ability to communicate over the Internet with low energy consumption. Thus, this work describes the necessary steps, challenges, and solutions for the development of an autonomous IoT node applicable to monitoring pressure and flow in a water supply network. In the second part of the work, the data collected by the IoT nodes was processed to eliminate outliers and used to train a model based on artificial neural networks that are capable of predicting the flow in the system under monitoring. The results show that, based on the data measured by the proposed IoT node, it is possible to predict the flow in distribution systems operating in real time. Full article

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32 pages, 4544 KB

Open AccessFeature PaperReview

A Review of Non-Invasive Continuous Blood Pressure Measurement: From Flexible Sensing to Intelligent Modeling

by Zhan Shen, Jian Li, Hao Hu, Chentao Du, Xiaorong Ding, Tingrui Pan and Xinge Yu

AI Sens. 2025, 1(2), 8; https://doi.org/10.3390/aisens1020008 - 7 Nov 2025

Cited by 7 | Viewed by 6919

Abstract

Accurate and continuous, non-invasive blood pressure (BP) monitoring plays a vital role in the long-term management of cardiovascular diseases. Advances in wearable and flexible sensing technologies have facilitated the transition of non-invasive BP monitoring from clinical settings to ambulatory home environments. However, the [...] Read more.

Accurate and continuous, non-invasive blood pressure (BP) monitoring plays a vital role in the long-term management of cardiovascular diseases. Advances in wearable and flexible sensing technologies have facilitated the transition of non-invasive BP monitoring from clinical settings to ambulatory home environments. However, the measurement consistency and algorithm adaptability of existing devices have not yet reached the level required for routine clinical practice. To address these limitations, comprehensive innovations have been made in material development, sensor design, and algorithm optimization. This review examines the evolution of non-invasive continuous BP measurement, highlighting cutting-edge advances in flexible electronic devices and BP estimation algorithms. First, we introduce measurement principles, sensing devices and limitations of traditional non-invasive BP measurement, including arterial tonometry, arterial volume clamp, and ultrasound-based methods. Subsequently, we review the pulse wave analysis-based BP estimation methods from two perspectives: flexible sensors based on optical, mechanical, and electrical principles, and estimation models that use physiological features or raw waveforms as input. Finally, we conclude the existing challenges and future development directions of flexible electronic technology and intelligent estimation algorithms for non-invasive continuous BP measurement. Full article

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23 pages, 2058 KB

Open AccessArticle

Inductive Displacement Sensor Operating in an LC Oscillator System Under High Pressure Conditions—Basic Design Principles

by Janusz Nurkowski and Andrzej Nowakowski

Sensors 2025, 25(19), 6078; https://doi.org/10.3390/s25196078 - 2 Oct 2025

Viewed by 1325

Abstract

The paper presents some design principles of an inductive displacement transducer for measuring the displacement of rock specimens under high hydrostatic pressure. It consists of a single-layer, coreless solenoid mounted directly onto the specimen and connected to an LC oscillator located outside the [...] Read more.

The paper presents some design principles of an inductive displacement transducer for measuring the displacement of rock specimens under high hydrostatic pressure. It consists of a single-layer, coreless solenoid mounted directly onto the specimen and connected to an LC oscillator located outside the pressure chamber, in which it serves as the inductive component. The specimen’s deformation changes the coil’s length and inductance, thereby altering the oscillator’s resonant frequency. Paired with a reference coil, the system achieves strain resolution of ~100 nm at pressures exceeding 400 MPa. Sensor design challenges include both electrical parameters (inductance and resistance of the sensor, capacitance of the resonant circuit) and mechanical parameters (number and diameter of coil turns, their positional stability, wire diameter). The basic requirement is to achieve stable oscillations (i.e., a high Q-factor of the resonant circuit) while maintaining maximum sensor sensitivity. Miniaturization of the sensor and minimizing the tensile force at its mounting points on the specimen are also essential. Improvement of certain sensor parameters often leads to the degradation of others; therefore, the design requires a compromise depending on the specific measurement conditions. This article presents the mathematical interdependencies among key sensor parameters, facilitating optimized sensor design. Full article

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