Sensors

28 pages, 3531 KiB

Open AccessReview

Review of Acoustic Emission Detection Technology for Valve Internal Leakage: Mechanisms, Methods, Challenges, and Application Prospects

by Dongjie Zheng, Xing Wang, Lingling Yang, Yunqi Li, Hui Xia, Haochuan Zhang and Xiaomei Xiang

Sensors 2025, 25(14), 4487; https://doi.org/10.3390/s25144487 - 18 Jul 2025

Abstract

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is [...] Read more.

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is capable of capturing the transient stress waves induced by leakage, thereby furnishing an effective means for the real-time monitoring and quantitative assessment of internal leakage within the valve body. This paper conducts a systematic review of the theoretical foundations, signal-processing methodologies, and the latest research advancements related to the technology for detecting internal leakage in the valve body based on acoustic emission. Firstly, grounded in Lechlier’s acoustic analogy theory, the generation mechanism of acoustic emission signals arising from valve body leakage is elucidated. Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. Moreover, this paper recapitulates the current challenges encountered by this technology and delineates future research orientations, such as the fusion of multi-modal sensors, the deployment of lightweight deep learning models, and integration with the Internet of Things. This study provides a systematic reference for the engineering application and theoretical development of the acoustic emission-based technology for detecting internal leakage in valves. Full article

(This article belongs to the Topic Advances in Non-Destructive Testing Methods, 3rd Edition)

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18 pages, 2062 KiB

Open AccessArticle

Measuring Blink-Related Brainwaves Using Low-Density Electroencephalography with Textile Electrodes for Real-World Applications

by Emily Acampora, Sujoy Ghosh Hajra and Careesa Chang Liu

Sensors 2025, 25(14), 4486; https://doi.org/10.3390/s25144486 - 18 Jul 2025

Abstract

Background: Electroencephalography (EEG) systems based on textile electrodes are increasingly being developed to address the need for more wearable sensor systems for brain function monitoring. Blink-related oscillations (BROs) are a new measure of brain function that corresponds to brainwave responses occurring after [...] Read more.

Background: Electroencephalography (EEG) systems based on textile electrodes are increasingly being developed to address the need for more wearable sensor systems for brain function monitoring. Blink-related oscillations (BROs) are a new measure of brain function that corresponds to brainwave responses occurring after spontaneous blinking, and indexes neural processes as the brain evaluates new visual information appearing after eye re-opening. Prior studies have reported BRO utility as both a clinical and non-clinical biomarker of cognition, but no study has demonstrated BRO measurement using textile-based EEG devices that facilitate user comfort for real-world applications. Methods: We investigated BRO measurement using a four-channel EEG system with textile electrodes by extracting BRO responses using existing, publicly available EEG data (n = 9). We compared BRO effects derived from textile-based electrodes with those from standard dry Ag/Ag-Cl electrodes collected at the same locations (i.e., Fp1, Fp2, F7, F8) and using the same EEG amplifier. Results: Results showed that BRO effects measured using textile electrodes exhibited similar features in both time and frequency domains compared to dry Ag/Ag-Cl electrodes. Data from both technologies also showed similar performance in artifact removal and signal capture. Conclusions: These findings provide the first demonstration of successful BRO signal capture using four-channel EEG with textile electrodes, providing compelling evidence toward the development of a comfortable and user-friendly EEG technology that uses the simple activity of blinking for objective brain function assessment in a variety of settings. Full article

(This article belongs to the Special Issue Latest Developments in Biopotential Sensor Development for Brain Signals)

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13 pages, 722 KiB

Open AccessArticle

Assessing Gross Motor and Gait Function Using Hip–Knee Cyclograms in Ambulatory Children with Spastic Cerebral Palsy

by Jehyun Yoo, Juntaek Hong, Jeuhee Lee, Yebin Cho, Taekyung Lee and Dong-wook Rha

Sensors 2025, 25(14), 4485; https://doi.org/10.3390/s25144485 - 18 Jul 2025

Abstract

Weakness, spasticity, and muscle shortening are common in children with cerebral palsy (CP), leading to deficits in gross motor, gait, and selective motor functions. While traditional assessments, such as the Gross Motor Function Measure (GMFM-66), instrumented gait analysis, and the Selective Control Assessment [...] Read more.

Weakness, spasticity, and muscle shortening are common in children with cerebral palsy (CP), leading to deficits in gross motor, gait, and selective motor functions. While traditional assessments, such as the Gross Motor Function Measure (GMFM-66), instrumented gait analysis, and the Selective Control Assessment of the Lower Extremity (SCALE), are widely used, they are often limited by the resource-intensive nature of hospital-based evaluations. We employed cyclogram-based analysis, utilizing simple hip and knee joint kinematics to assess clinical measures, including GMFM-66, normalized gait speed, the gait deviation index (GDI), and the gait profile score (GPS). Principal component analysis was used to quantify the cyclogram shape characteristics. A total of 144 children with ambulatory spastic CP were included in the study. All the cyclogram parameters were significantly correlated with GMFM-66, gait speed, the GDI, and the sagittal plane subscore of the GPS for the hip and knee, with the swing phase area showing the strongest correlation. Regression models based on the swing phase area were used to estimate the GMFM-66 (R² = 0.301) and gait speed (R² = 0.484). The PC1/PC2 ratio showed a moderate correlation with selective motor control, as measured by the SCALE (R² = 0.320). These findings highlight the potential of hip–knee cyclogram parameters to be used as accessible digital biomarkers for evaluating motor control and gait function in children with bilateral spastic CP. Further prospective studies using wearable sensors, such as inertial measurement units, are warranted to validate and build upon these results. Full article

(This article belongs to the Section Physical Sensors)

32 pages, 2402 KiB

Open AccessArticle

Post-Quantum Linkable Hash-Based Ring Signature Scheme for Off-Chain Payments in IoT

by Linlin He, Xiayi Zhou, Dongqin Cai, Xiao Hu and Shuanggen Liu

Sensors 2025, 25(14), 4484; https://doi.org/10.3390/s25144484 - 18 Jul 2025

Abstract

Off-chain payments in the Internet of Things (IoT) enhance the efficiency and scalability of blockchain transactions. However, existing privacy mechanisms face challenges, such as the disclosure of payment channels and transaction traceability. Additionally, the rise of quantum computing threatens traditional public key cryptography, [...] Read more.

Off-chain payments in the Internet of Things (IoT) enhance the efficiency and scalability of blockchain transactions. However, existing privacy mechanisms face challenges, such as the disclosure of payment channels and transaction traceability. Additionally, the rise of quantum computing threatens traditional public key cryptography, making the development of post quantum secure methods for privacy protection essential. This paper proposes a post-quantum ring signature scheme based on hash functions that can be applied to off-chain payments, enhancing both anonymity and linkability. The scheme is designed to resist quantum attacks through the use of hash-based signatures and to prevent double spending via its linkable properties. Furthermore, the paper introduces an improved Hash Time-Locked Contract (HTLC) that incorporates a Signature of Knowledge (SOK) to conceal the payment path and strengthen privacy protection. Security analysis and experimental evaluations demonstrate that the system strikes a favorable balance between privacy, computational efficiency, and security. Notably, the efficiency benefits of basic signature verification are particularly evident, offering new insights into privacy protection for post-quantum secure blockchain. Full article

(This article belongs to the Special Issue Blockchain-Based Security System Applied to IoT Used in Intelligent Systems)

18 pages, 5282 KiB

Open AccessArticle

A 1000 fps High-Dynamic-Range Global Shutter CMOS Image Sensor with Full Thermometer Code Current-Steering Ramp

by Liqiang Han, Ganlin Cheng, Xu Zhang, Gengyun Wang, Weijun Pan, Yao Yao, Guihai Yu, Ruimeng Zhang, Shuaichen Mu, Songbo Wu, Hongbo Bu, Liqun Dai, Ben Fan, Dan Wang, Wei Fan and Ruiming Chen

Sensors 2025, 25(14), 4483; https://doi.org/10.3390/s25144483 - 18 Jul 2025

Abstract

We present a 1024 × 512, 1000 fps, high-dynamic-range global shutter CMOS image sensor. The pixel is based on a voltage domain global shutter architecture, featuring a pitch of 24 μm × 24 μm. Both high-gain and low-gain signals can be captured within [...] Read more.

We present a 1024 × 512, 1000 fps, high-dynamic-range global shutter CMOS image sensor. The pixel is based on a voltage domain global shutter architecture, featuring a pitch of 24 μm × 24 μm. Both high-gain and low-gain signals can be captured within a single frame. The combined dynamic range is 95 dB, and the full well capacity is 620 ke-. In this paper, we analyze the pixel noise performance as well as the non-linearity and image lag that arise from parasitic capacitance in the pixel. The ramp generator is based on a 12-bit full thermometer code current-steering DAC with high driving capability. We discuss the design considerations for the ramp generator, particularly addressing the phenomenon of non-linear response. Finally, the comparator design and the column readout noise are analyzed. Full article

(This article belongs to the Section Electronic Sensors)

20 pages, 4148 KiB

Open AccessArticle

Automated Discrimination of Appearance Quality Grade of Mushroom (Stropharia rugoso-annulata) Using Computer Vision-Based Air-Blown System

by Meng Lv, Lei Kong, Qi-Yuan Zhang and Wen-Hao Su

Sensors 2025, 25(14), 4482; https://doi.org/10.3390/s25144482 - 18 Jul 2025

Abstract

The mushroom Stropharia rugoso-annulata is one of the most popular varieties in the international market because it is highly nutritious and has a delicious flavor. However, grading is still performed manually, leading to inconsistent grading standards and low efficiency. In this study, deep [...] Read more.

The mushroom Stropharia rugoso-annulata is one of the most popular varieties in the international market because it is highly nutritious and has a delicious flavor. However, grading is still performed manually, leading to inconsistent grading standards and low efficiency. In this study, deep learning and computer vision techniques were used to develop an automated air-blown grading system for classifying this mushroom into three quality grades. The system consisted of a classification module and a grading module. In the classification module, the cap and stalk regions were extracted using the YOLOv8-seg algorithm, then post-processed using OpenCV based on quantitative grading indexes, forming the proposed SegGrade algorithm. In the grading module, an air-blown grading system with an automatic feeding unit was developed in combination with the SegGrade algorithm. The experimental results show that for 150 randomly selected mushrooms, the trained YOLOv8-seg algorithm achieved an accuracy of 99.5% in segmenting the cap and stalk regions, while the SegGrade algorithm achieved an accuracy of 94.67%. Furthermore, the system ultimately achieved an average grading accuracy of 80.66% and maintained the integrity of the mushrooms. This system can be further expanded according to production needs, improving sorting efficiency and meeting market demands. Full article

(This article belongs to the Special Issue Sensing Technology and Computer Vision for Precision Agriculture and Smart Farming)

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33 pages, 2217 KiB

Open AccessReview

A Comprehensive Review of Artificial Intelligence-Based Algorithms for Predicting the Remaining Useful Life of Equipment

by Weihao Li, Jianhua Chen, Sijuan Chen, Peilin Li, Bing Zhang, Ming Wang, Ming Yang, Jipu Wang, Dejian Zhou and Junsen Yun

Sensors 2025, 25(14), 4481; https://doi.org/10.3390/s25144481 - 18 Jul 2025

Abstract

In the contemporary big data era, data-driven prognostic and health management (PHM) methodologies have emerged as indispensable tools for ensuring the secure and reliable operation of complex equipment systems. Central to these methodologies is the accurate prediction of remaining useful life (RUL), which [...] Read more.

In the contemporary big data era, data-driven prognostic and health management (PHM) methodologies have emerged as indispensable tools for ensuring the secure and reliable operation of complex equipment systems. Central to these methodologies is the accurate prediction of remaining useful life (RUL), which serves as a pivotal cornerstone for effective maintenance and operational decision-making. While significant advancements in computer hardware and artificial intelligence (AI) algorithms have catalyzed substantial progress in AI-based RUL prediction, extant research frequently exhibits a narrow focus on specific algorithms, neglecting a comprehensive and comparative analysis of AI techniques across diverse equipment types and operational scenarios. This study endeavors to bridge this gap through the following contributions: (1) A rigorous analysis and systematic categorization of application scenarios for equipment RUL prediction, elucidating their distinct characteristics and requirements. (2) A comprehensive summary and comparative evaluation of several AI algorithms deemed suitable for RUL prediction, delineating their respective strengths and limitations. (3) An in-depth comparative analysis of the applicability of AI algorithms across varying application contexts, informed by a nuanced understanding of different application scenarios and AI algorithm research. (4) An insightful discussion on the current challenges confronting AI-based RUL prediction technology, coupled with a forward-looking examination of its future prospects. By furnishing a meticulous and holistic understanding of the traits of various AI algorithms and their contextual applicability, this study aspires to facilitate the attainment of optimal application outcomes in the realm of equipment RUL prediction. Full article

(This article belongs to the Section Intelligent Sensors)

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20 pages, 18517 KiB

Open AccessArticle

A Highly Sensitive Low-Temperature N-Butanol Gas Sensor Based on a Co-Doped MOF-ZnO Nanomaterial Under UV Excitation

by Yinzhong Liu, Xiaoshun Wei, Yun Guo, Lingchao Wang, Hui Guo, Qingjie Wang, Yiyu Qiao, Xiaotao Zhu, Xuechun Yang, Lingli Cheng and Zheng Jiao

Sensors 2025, 25(14), 4480; https://doi.org/10.3390/s25144480 - 18 Jul 2025

Abstract

Volatile organic compounds (VOCs) are presently posing a rather considerable threat to both human health and environmental sustainability. Among these, n-butanol is commonly identified as bringing potential hazards to environmental integrity and individual health. This study presents the creation of a highly sensitive [...] Read more.

Volatile organic compounds (VOCs) are presently posing a rather considerable threat to both human health and environmental sustainability. Among these, n-butanol is commonly identified as bringing potential hazards to environmental integrity and individual health. This study presents the creation of a highly sensitive n-butanol gas sensor utilizing cobalt-doped zinc oxide (ZnO) derived from a metal–organic framework (MOF). A series of x-Co/MOF-ZnO (x = 1, 3, 5, 7 wt%) nanomaterials with varying Co ratios were generated using the homogeneous co-precipitation method and assessed for their gas-sensing performances under a low operating temperature (191 °C) and UV excitation (220 mW/cm²). These findings demonstrated that the 5-Co/MOF-ZnO sensor presented the highest oxygen vacancy (O_v) concentration and the largest specific surface area (SSA), representing the optimal reactivity, selectivity, and durability for n-butanol detection. Regarding the sensor’s response to 100 ppm n-butanol under UV excitation, it achieved a value of 1259.06, 9.80 times greater than that of pure MOF-ZnO (128.56) and 2.07 times higher than that in darkness (608.38). Additionally, under UV illumination, the sensor achieved a rapid response time (11 s) and recovery rate (23 s). As a strategy to transform the functionality of ZnO-based sensors for n-butanol gas detection, this study also investigated potential possible redox reactions occurring during the detection process. Full article

(This article belongs to the Special Issue New Sensors Based on Inorganic Material)

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24 pages, 2603 KiB

Open AccessArticle

Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery

by Xiaoyu Hu, Xiuyuan Zhao and Wenhe Liu

Sensors 2025, 25(14), 4479; https://doi.org/10.3390/s25144479 - 18 Jul 2025

Abstract

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale [...] Read more.

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

(This article belongs to the Special Issue Functional Polymers and Fibers: Sensing Materials and Applications)

23 pages, 3725 KiB

Open AccessArticle

Influence of Surface Isolation Layers on High-Voltage Tolerance of Small-Pitch 3D Pixel Sensors

by Jixing Ye and Gian-Franco Dalla Betta

Sensors 2025, 25(14), 4478; https://doi.org/10.3390/s25144478 - 18 Jul 2025

Abstract

In recent years, 3D pixel sensors have been a topic of increasing interest within the High Energy Physics community. Due to their inherent radiation hardness, demonstrated up to a fluence of

3 e 16

1MeV equivalent neutrons per square centimeter, 3D pixel sensors [...] Read more.

In recent years, 3D pixel sensors have been a topic of increasing interest within the High Energy Physics community. Due to their inherent radiation hardness, demonstrated up to a fluence of

3 e 16

1MeV equivalent neutrons per square centimeter, 3D pixel sensors have been used to equip the innermost tracking layers of the ATLAS and CMS detector upgrades at the High-Luminosity Large Hadron Collider. Additionally, the next generation of vertex detectors calls for precise measurement of charged particle timing at the pixel level. Owing to their fast response times, 3D sensors present themselves as a viable technology for these challenging applications. Nevertheless, both radiation hardness and fast timing require 3D sensors to be operated with high bias voltages on the order of ∼150 V and beyond. Special attention should therefore be devoted to avoiding problems that could cause premature electrical breakdown, which could limit sensor performance. In this paper, TCAD simulations are used to gain deep insight into the impact of surface isolation layers (i.e., p-stop and p-spray) used by different vendors on the high-voltage tolerance of small-pitch 3D sensors. Results relevant to different geometrical configurations and irradiation scenarios are presented. The advantages and disadvantages of the available technologies are discussed, offering guidance for design optimization. Experimentalmeasurements from existing samples based on both isolation techniques show good agreement with simulated breakdown voltages, thereby validating the simulation approach. Full article

(This article belongs to the Special Issue Feature Papers in Physical Sensors 2025)

15 pages, 2540 KiB

Open AccessArticle

Experimental Analysis on the Effect of Contact Pressure and Activity Level as Influencing Factors in PPG Sensor Performance

by Francesco Scardulla, Gloria Cosoli, Cosmina Gnoffo, Luca Antognoli, Francesco Bongiorno, Gianluca Diana, Lorenzo Scalise, Leonardo D’Acquisto and Marco Arnesano

Sensors 2025, 25(14), 4477; https://doi.org/10.3390/s25144477 - 18 Jul 2025

Abstract

Photoplethysmographic (PPG) sensors are small and cheap wearable sensors which open the possibility of monitoring physiological parameters such as heart rate during normal daily routines, ultimately providing valuable information on health status. Despite their potential and distribution within wearable devices, their accuracy is [...] Read more.

Photoplethysmographic (PPG) sensors are small and cheap wearable sensors which open the possibility of monitoring physiological parameters such as heart rate during normal daily routines, ultimately providing valuable information on health status. Despite their potential and distribution within wearable devices, their accuracy is affected by several influencing parameters, such as contact pressure and physical activity. In this study, the effect of contact pressure (i.e., at 20, 60, and 75 mmHg) and intensity of physical activity (i.e., at 3, 6, and 8 km/h) were evaluated on a sample of 25 subjects using both a reference device (i.e., an electrocardiography-based device) and a PPG sensor applied to the skin with controlled contact pressure values. Results showed differing accuracy and precision when measuring the heart rate at different pressure levels, achieving the best performance at a contact pressure of 60 mmHg, with a mean absolute percentage error of between 3.36% and 6.83% depending on the physical activity levels, and a Pearson’s correlation coefficient of between 0.81 and 0.95. Plus, considering the individual optimal contact pressure, measurement uncertainty significantly decreases at any contact pressure, for instance, decreasing from 15 bpm (at 60 mmHg) to 8 bpm when running at a speed of 6 km/h (coverage factor k = 2). These results may constitute useful information for both users and manufacturers to improve the metrological performance of PPG sensors and expand their use in a clinical context. Full article

(This article belongs to the Special Issue Sensors Network and Wearables for People Activities and Wellbeing Monitoring)

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25 pages, 6248 KiB

Open AccessArticle

Low-Cost Strain-Gauge Force-Sensing Sidestick for 6-DoF Flight Simulation: Design and Human-in-the-Loop Evaluation

by Patrik Rožić, Milan Vrdoljak, Karolina Krajček Nikolić and Jurica Ivošević

Sensors 2025, 25(14), 4476; https://doi.org/10.3390/s25144476 - 18 Jul 2025

Abstract

Modern fly-by-wire (FBW) aircraft demand high-fidelity simulation systems for research and training, yet existing force-sensing solutions are often prohibitively expensive. This study presents the design, development, and validation of a low-cost, reconfigurable force-sensing sidestick. The system utilizes four strain-gauge load cells to capture [...] Read more.

Modern fly-by-wire (FBW) aircraft demand high-fidelity simulation systems for research and training, yet existing force-sensing solutions are often prohibitively expensive. This study presents the design, development, and validation of a low-cost, reconfigurable force-sensing sidestick. The system utilizes four strain-gauge load cells to capture pure pilot force inputs, integrated with a 6-DoF non-linear flight model. To evaluate its performance, a pitch-angle tracking task was conducted with 16 participants (pilots and non-pilots). Objective metrics revealed that the control strategy was a primary determinant of performance. Participants employing a proactive feedforward control strategy exhibited roughly an order of magnitude lower tracking-error variance than those relying on reactive corrections. Subjective assessments using the Cooper-Harper scale and NASA-TLX corroborated the objective data, confirming the sidestick’s ability to differentiate control techniques. This work demonstrates an open-source platform that makes high-fidelity FBW simulation accessible for academic research, pilot training, and human factors analysis at a fraction of the cost of commercial systems. Full article

(This article belongs to the Topic Innovation and Inventions in Aerospace and UAV Applications)

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20 pages, 5236 KiB

Open AccessArticle

Leakage Detection in Subway Tunnels Using 3D Point Cloud Data: Integrating Intensity and Geometric Features with XGBoost Classifier

by Anyin Zhang, Junjun Huang, Zexin Sun, Juju Duan, Yuanai Zhang and Yueqian Shen

Sensors 2025, 25(14), 4475; https://doi.org/10.3390/s25144475 - 18 Jul 2025

Abstract

Detecting leakage using a point cloud acquired by mobile laser scanning (MLS) presents significant challenges, particularly from within three-dimensional space. These challenges primarily arise from the prevalence of noise in tunnel point clouds and the difficulty in accurately capturing the three-dimensional morphological characteristics [...] Read more.

Detecting leakage using a point cloud acquired by mobile laser scanning (MLS) presents significant challenges, particularly from within three-dimensional space. These challenges primarily arise from the prevalence of noise in tunnel point clouds and the difficulty in accurately capturing the three-dimensional morphological characteristics of leakage patterns. To address these limitations, this study proposes a classification method based on XGBoost classifier, integrating both intensity and geometric features. The proposed methodology comprises the following steps: First, a RANSAC algorithm is employed to filter out noise from tunnel objects, such as facilities, tracks, and bolt holes, which exhibit intensity values similar to leakage. Next, intensity features are extracted to facilitate the initial separation of leakage regions from the tunnel lining. Subsequently, geometric features derived from the k neighborhood are incorporated to complement the intensity features, enabling more effective segmentation of leakage from the lining structures. The optimal neighborhood scale is determined by selecting the scale that yields the highest F_1-score for leakage across various multiple evaluated scales. Finally, the XGBoost classifier is applied to the binary classification to distinguish leakage from tunnel lining. Experimental results demonstrate that the integration of geometric features significantly enhances leakage detection accuracy, achieving an F_1-score of 91.18% and 97.84% on two evaluated datasets, respectively. The consistent performance across four heterogeneous datasets indicates the robust generalization capability of the proposed methodology. Comparative analysis further shows that XGBoost outperforms other classifiers, such as Random Forest, AdaBoost, LightGBM, and CatBoost, in terms of balance of accuracy and computational efficiency. Moreover, compared to deep learning models, including PointNet, PointNet++, and DGCNN, the proposed method demonstrates superior performance in both detection accuracy and computational efficiency. Full article

(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)

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22 pages, 3235 KiB

Open AccessArticle

Advanced Multi-Scale CNN-BiLSTM for Robust Photovoltaic Fault Detection

by Xiaojuan Zhang, Bo Jing, Xiaoxuan Jiao and Ruixu Yao

Sensors 2025, 25(14), 4474; https://doi.org/10.3390/s25144474 - 18 Jul 2025

Abstract

The increasing deployment of photovoltaic (PV) systems necessitates robust fault detection mechanisms to ensure operational reliability and safety. Conventional approaches, however, struggle in complex industrial environments characterized by high noise, data incompleteness, and class imbalance. This study proposes an innovative Advanced CNN-BiLSTM architecture [...] Read more.

The increasing deployment of photovoltaic (PV) systems necessitates robust fault detection mechanisms to ensure operational reliability and safety. Conventional approaches, however, struggle in complex industrial environments characterized by high noise, data incompleteness, and class imbalance. This study proposes an innovative Advanced CNN-BiLSTM architecture integrating multi-scale feature extraction with hierarchical attention to enhance PV fault detection. The proposed framework employs four parallel CNN branches with kernel sizes of 3, 7, 15, and 31 to capture temporal patterns across various time scales. These features are then integrated by an adaptive feature fusion network that utilizes multi-head attention. A two-layer bidirectional LSTM with temporal attention mechanism processes the fused features for final classification. Comprehensive evaluation on the GPVS-Faults dataset using a progressive difficulty validation framework demonstrates exceptional performance improvements. Under extreme industrial conditions, the proposed method achieves 83.25% accuracy, representing a substantial 119.48% relative improvement over baseline CNN-BiLSTM (37.93%). Ablation studies reveal that the multi-scale CNN contributes 28.0% of the total performance improvement, while adaptive feature fusion accounts for 22.0%. Furthermore, the proposed method demonstrates superior robustness under severe noise (σ = 0.20), high levels of missing data (15%), and significant outlier contamination (8%). These characteristics make the architecture highly suitable for real-world industrial deployment and establish a new paradigm for temporal feature fusion in renewable energy fault detection. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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36 pages, 4475 KiB

Open AccessArticle

Technical Condition Assessment of Light-Alloy Wheel Rims Based on Acoustic Parameter Analysis Using a Neural Network

by Arkadiusz Rychlik

Sensors 2025, 25(14), 4473; https://doi.org/10.3390/s25144473 - 18 Jul 2025

Abstract

Light alloy wheel rims, despite their widespread use, remain vulnerable to fatigue-related defects and mechanical damage. This study presents a method for assessing their technical condition based on acoustic parameter analysis and classification using a deep neural network. Diagnostic data were collected using [...] Read more.

Light alloy wheel rims, despite their widespread use, remain vulnerable to fatigue-related defects and mechanical damage. This study presents a method for assessing their technical condition based on acoustic parameter analysis and classification using a deep neural network. Diagnostic data were collected using a custom-developed ADF (Acoustic Diagnostic Features) system, incorporating the reverberation time (T₆₀), sound absorption coefficient (α), and acoustic energy (E). These parameters were measured during laboratory fatigue testing on a Wheel Resistance Test Rig (WRTR) and from used rims obtained under real-world operating conditions. The neural network was trained on WRTR data and subsequently employed to classify field samples as either “serviceable” or “unserviceable”. Results confirmed the high effectiveness of the proposed method, including its robustness in detecting borderline cases, as demonstrated in a case study involving a mechanically damaged rim. The developed approach offers potential support for diagnostic decision-making in workshop settings and may, in the future, serve as a foundation for sensor-based real-time rim condition monitoring. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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26 pages, 54898 KiB

Open AccessArticle

MSWF: A Multi-Modal Remote Sensing Image Matching Method Based on a Side Window Filter with Global Position, Orientation, and Scale Guidance

by Jiaqing Ye, Guorong Yu and Haizhou Bao

Sensors 2025, 25(14), 4472; https://doi.org/10.3390/s25144472 - 18 Jul 2025

Abstract

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window [...] Read more.

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks. Full article

(This article belongs to the Section Remote Sensors)

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12 pages, 1770 KiB

Open AccessArticle

Measuring the Operating Condition of Induction Motor Using High-Sensitivity Magnetic Sensor

by Akane Kobayashi, Kenji Nakamura and Takahito Ono

Sensors 2025, 25(14), 4471; https://doi.org/10.3390/s25144471 - 18 Jul 2025

Abstract

This study aimed to monitor the operating state of an induction motor, a type of electromagnetic motor, using a highly sensitive magnetic sensor, which could be applied for anomaly detection in the future. Monitoring the health of electromagnetic motors is very important to [...] Read more.

This study aimed to monitor the operating state of an induction motor, a type of electromagnetic motor, using a highly sensitive magnetic sensor, which could be applied for anomaly detection in the future. Monitoring the health of electromagnetic motors is very important to minimize losses due to failures. Detecting anomalies using the changes compared with the initial state is a possible solution, but there are issues such as a lack of training data for machine learning and the need to install multiple sensors. Therefore, an attempt was made to acquire the various operating states of a motor from magnetic signals using a single magnetic sensor capable of non-contact measurement. The relationships between the magnetic flux density from the motor and the other motor conditions were investigated. As a result, the magnetic spectrum was found to contain information on the rotor rotation frequency, torque, and output power. Therefore, the magnetic sensor can be applied to monitor a motor’s operating conditions, making it a useful tool for advanced data analysis. Full article

(This article belongs to the Section Industrial Sensors)

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11 pages, 1540 KiB

Open AccessArticle

Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones

by Aske G. Larsen, Line Ø. Sadolin, Trine R. Thomsen and Anderson S. Oliveira

Sensors 2025, 25(14), 4470; https://doi.org/10.3390/s25144470 - 18 Jul 2025

Abstract

As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of [...] Read more.

As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of 52 participants walked at three speeds while carrying a smartphone-sized IMU in natural positions (hand, trouser pocket, or jacket pocket). A previously trained Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based machine learning model predicted gait events, which were then used to calculate stride time, stance time, swing time, and double support time. Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). Despite these variations, moderate-to-strong correlations between the predicted and experimental spatiotemporal gait parameters suggest the feasibility of IMU-based gait tracking in real-world settings. These associations preserved inter-subject patterns that are relevant for detecting gait disorders. Our study demonstrated the feasibility of extracting clinically relevant gait parameters using IMU data mimicking smartphone use, especially parameters with longer durations such as stride time. Robustness across sensor locations and walking speeds supports deep learning on single-IMU data as a viable tool for remote gait monitoring. Full article

(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))

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12 pages, 5437 KiB

Open AccessArticle

Cyber-Physical System Interface for Implantable Esophageal Prosthesis

by Ana Magdalena Anghel and Teodora Mîndra

Sensors 2025, 25(14), 4469; https://doi.org/10.3390/s25144469 - 18 Jul 2025

Abstract

This article presents a Cyber-Physical System Interface (CPSI) for a patented implantable esophageal prosthesis. Designed for in vivo use, the CPSI has been implemented in a MATLAB (version R2021b) simulation environment integrated with real-time data from sensors relevant for monitoring the prosthesis’s physical [...] Read more.

This article presents a Cyber-Physical System Interface (CPSI) for a patented implantable esophageal prosthesis. Designed for in vivo use, the CPSI has been implemented in a MATLAB (version R2021b) simulation environment integrated with real-time data from sensors relevant for monitoring the prosthesis’s physical positioning and environmental interactions, aggregated through an Arduino external system. This setup enables the modeling and analysis of system behaviors in a controlled setting. The paper discusses the sensors, hardware and software components supporting a wide range of applications, and the method chosen for sensor-to-display flow. The case study demonstrates two monitoring system applications: one analyzes the influence of variations in the prosthesis geometry, while the other evaluates the tissue response to the implant. The proposed framework and implementation are highly relevant for a wide range of in vivo implants and related systems. Full article

(This article belongs to the Section Physical Sensors)

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