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Intelligent Sensors and Signal Processing in Industry

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5074

Special Issue Editors

Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: magnetic flux leakage testing; electromagnetic ultrasonic guided wave testing; defect inversion imaging; signal processing; intelligent sensors
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Guest Editor
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
Interests: non-destructive evaluation; ultrasonics; structural health monitoring; guided waves; measurements and instrumentation; FE modeling; microcontrollers; composite structures
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Guest Editor
School of Physical Science and Engineering, Beijing Jiaotong University, 100044, China
Interests: ultrasonic non-destructive testing; rail transit; intelligent sensing

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Guest Editor
School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: electromagnetic sensors; electromagnetic ultrasonic non-destructive testing; intelligent imaging

Special Issue Information

Dear Colleagues,

The integration of intelligent sensors and advanced signal processing techniques in industry is revolutionizing traditional manufacturing and operational processes, ushering in a new era of efficiency, precision, and automation. Intelligent sensors, equipped with capabilities such as self-diagnostics, data processing, and communication, are pivotal in transforming raw data into actionable insights. These sensors are employed across a wide range of industrial applications, from monitoring machinery health and predicting failures to optimizing energy consumption and ensuring product quality. Complementing intelligent sensors is advanced signal processing technology. Through real-time data analysis, noise reduction, pattern recognition, and modern artificial intelligence techniques, these technologies further enhance the capabilities of intelligent sensors, enabling more accurate and reliable decision making. It is evident that intelligent sensors and signal processing technology hold significant importance in industries. Their integration brings about more efficient, precise, and automated production methods, driving industrial development and progress. By delving deeper into the exploration and application of intelligent sensors and signal processing technology, we can further enhance industrial competitiveness and achieve sustainable development.

This Special Issue aims to explore the cutting-edge advancements and applications of intelligent sensors and signal processing in various industrial contexts.

In this Special Issue, we look forward to receiving papers on a wide range of research topics, including the following:

  • New materials, technologies, and designs for intelligent sensors.
  • Application of intelligent sensors in NDT, SHM, and fault warning.
  • Various NDT technologies in electric energy, petroleum, transportation, construction, chemical industry, and special equipment.
  • Development and deployment of intelligent sensors in industrial environments.
  • Signal processing technologies in industrial automation and intelligent manufacturing.
  • Machine learning and AI techniques for predictive maintenance and anomaly detection.
  • Sensor fusion and integration in industrial IoT (IIoT) systems.
  • Case studies demonstrating the impact of intelligent sensors on operational efficiency and safety.
  • Emerging trends and future directions in industrial sensor technology.

Dr. Lisha Peng
Dr. Oleksii Karpenko
Dr. Hongyu Sun
Dr. Zhichao Cai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • non-destructive testing
  • structural health monitoring
  • intelligent sensors
  • signal processing
  • industrial applications
  • predictive maintenance
  • machine learning
  • real-time monitoring
  • Industrial IoT (IIoT)

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Published Papers (6 papers)

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Research

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16 pages, 11828 KiB  
Article
A Precise Oxide Film Thickness Measurement Method Based on Swept Frequency and Transmission Cable Impedance Correction
by Yifan Li, Qi Xiao, Lisha Peng, Songling Huang and Chaofeng Ye
Sensors 2025, 25(2), 579; https://doi.org/10.3390/s25020579 - 20 Jan 2025
Viewed by 185
Abstract
Accurately measuring the thickness of the oxide film that accumulates on nuclear fuel assemblies is critical for maintaining nuclear power plant safety. Oxide film thickness typically ranges from a few micrometers to several tens of micrometers, necessitating a high-precision measurement system. Eddy current [...] Read more.
Accurately measuring the thickness of the oxide film that accumulates on nuclear fuel assemblies is critical for maintaining nuclear power plant safety. Oxide film thickness typically ranges from a few micrometers to several tens of micrometers, necessitating a high-precision measurement system. Eddy current testing (ECT) is commonly employed during poolside inspections due to its simplicity and ease of on-site implementation. The use of swept frequency technology can mitigate the impact of interference parameters and improve the measurement accuracy of ECT. However, as the nuclear assembly is placed in a pool for inspection, a cable several dozen meters in length is used to connect the ECT probe to the instrument. The measurement is affected by the transmission line and its effect is a function of the operating frequencies, resulting in errors for swept frequency measurements. This paper proposes a method for precisely measuring oxide film thickness based on the swept frequency technique and long transmission line impedance correction. The signals are calibrated based on a transmission line model of the cable, effectively eliminating the influence of the transmission cable. A swept frequency signal-processing algorithm is developed to separate the parameters and calculate oxide film thickness. To verify the feasibility of the method, measurements are conducted on fuel cladding samples with varying conductivities. It is found that the method can accurately assess oxide film thickness with varying conductivity. The maximum error is 3.42 μm, while the average error is 1.82 μm. The impedance correction reduces the error by 66%. The experimental results indicate that this method can eliminate the impact of long transmission cables, and the algorithm can mitigate the influence of material conductivity. This method can be utilized to measure oxide film thickness in nuclear power maintenance inspections following extensive testing and engineering optimization. Full article
(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry)
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29 pages, 3092 KiB  
Article
A Comparison Study of Person Identification Using IR Array Sensors and LiDAR
by Kai Liu, Mondher Bouazizi, Zelin Xing and Tomoaki Ohtsuki
Sensors 2025, 25(1), 271; https://doi.org/10.3390/s25010271 - 6 Jan 2025
Viewed by 424
Abstract
Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities—RGB, thermal, and depth—using datasets collected with infrared array [...] Read more.
Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities—RGB, thermal, and depth—using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 × 12 to 640 × 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation. Results show a similar identification performance between the three modalities, in particular in high resolution (i.e., 640 × 480), with RGB image classification reaching 100.0%, depth images reaching 99.54% and thermal images reaching 97.93%. However, upon deeper investigation, thermal images show more robustness and generalizability by maintaining focus on subject-specific features even at low resolutions. In contrast, RGB data performs well at high resolutions but exhibits reliance on background features as resolution decreases. Depth data shows significant degradation at lower resolutions, suffering from scattered attention and artifacts. These findings highlight the importance of modality selection, with thermal imaging emerging as the most reliable. Future work will explore multi-modal integration, advanced preprocessing, and hybrid architectures to enhance model adaptability and address current limitations. This study highlights the potential of thermal imaging and the need for modality-specific strategies in designing robust person identification systems. Full article
(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry)
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17 pages, 1313 KiB  
Article
An Efficient Anomalous Sound Detection System for Microcontrollers
by Yi-Cheng Lo, Tsung-Lin Tsai, Chieh-Wen Yang and An-Yeu Wu
Sensors 2024, 24(23), 7478; https://doi.org/10.3390/s24237478 - 23 Nov 2024
Viewed by 465
Abstract
Anomalous Sound Detection (ASD) systems are pivotal in the Industrial Internet of Things (IIoT). Through the early detection of machines’ anomalies, these systems facilitate proactive maintenance, thereby mitigating potential losses. Although prior studies have improved system accuracy using various advanced machine learning technologies, [...] Read more.
Anomalous Sound Detection (ASD) systems are pivotal in the Industrial Internet of Things (IIoT). Through the early detection of machines’ anomalies, these systems facilitate proactive maintenance, thereby mitigating potential losses. Although prior studies have improved system accuracy using various advanced machine learning technologies, they frequently neglect the associated substantial computing and storage demands, which are crucial in resource-constrained IIoT environments. In this paper, we propose an ASD system that is efficiently optimized for both software and hardware considerations regarding edge intelligence. For the software aspect, we identify signal variation as a critical issue for ASD. Hence, we introduce a suite of lightweight yet robust processing techniques that enhance accuracy while minimizing resource consumption. As for the hardware aspect, we find that memory constraints may be a significant challenge for deploying ASD systems on microcontrollers (MCUs). Therefore, we propose a memory-aware pruning algorithm specialized for ASD to fit into MCUs’ constraints. Finally, we evaluate our method on the DCASE dataset, and the results show that our system achieves favorable outcomes in both accuracy and resource efficiency, marking our contribution to ASD system practice. Full article
(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry)
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11 pages, 5506 KiB  
Article
Crack Detection Method for Wind Turbine Tower Bolts Using Ultrasonic Spiral Phased Array
by Hongyu Sun, Jingqi Dong, Xi Diao, Xincheng Huang, Ziyi Huang and Zhichao Cai
Sensors 2024, 24(16), 5204; https://doi.org/10.3390/s24165204 - 11 Aug 2024
Viewed by 1394
Abstract
High-strength bolts are crucial load-bearing components of wind turbine towers. They are highly susceptible to fatigue cracks over long-term service and require timely detection. However, due to the structural complexity and hidden nature of the cracks in wind turbine tower bolts, the small [...] Read more.
High-strength bolts are crucial load-bearing components of wind turbine towers. They are highly susceptible to fatigue cracks over long-term service and require timely detection. However, due to the structural complexity and hidden nature of the cracks in wind turbine tower bolts, the small size of the cracks, and their variable propagation directions, detection signals carrying crack information are often drowned out by dense thread signals. Existing non-destructive testing methods are unable to quickly and accurately characterize small cracks at the thread roots. Therefore, we propose an ultrasonic phased array element arrangement method based on the Fermat spiral array. This method can greatly increase the fill rate of the phased array with small element spacing while reducing the effects of grating and sidelobes, thereby achieving high-energy excitation and accurate imaging with the ultrasonic phased array. This has significant theoretical and engineering application value for ensuring the safe and reliable service of key wind turbine components and for promoting the technological development of the wind power industry. Full article
(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry)
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Review

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15 pages, 4619 KiB  
Review
A Review of Asynchronous Byzantine Consensus Protocols
by Zhenyan Ji, Xiao Zhang, Jianghao Hu, Yuan Lu and Jiqiang Liu
Sensors 2024, 24(24), 7927; https://doi.org/10.3390/s24247927 - 11 Dec 2024
Viewed by 685
Abstract
Blockchain technology can be used in the IoT to ensure the data privacy collected by sensors. In blockchain systems, consensus mechanisms are a key technology for maintaining data consistency and correctness. Among the various consensus protocols, asynchronous Byzantine consensus protocols offer strong robustness [...] Read more.
Blockchain technology can be used in the IoT to ensure the data privacy collected by sensors. In blockchain systems, consensus mechanisms are a key technology for maintaining data consistency and correctness. Among the various consensus protocols, asynchronous Byzantine consensus protocols offer strong robustness as they do not rely on any network timing assumptions during design. As a result, these protocols have become a research hotspot in the field of blockchain. Based on different structural design approaches, asynchronous Byzantine consensus protocols can be divided into two categories: protocols based on the DAG structure and protocols based on the ACS structure. The paper describes their principles and summarizes the related research works. The advantages and disadvantages of the protocols are also compared and analyzed. At the end of the paper, future research directions are identified. Full article
(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry)
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Other

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50 pages, 2370 KiB  
Systematic Review
Movement Disorders and Smart Wrist Devices: A Comprehensive Study
by Andrea Caroppo, Andrea Manni, Gabriele Rescio, Anna Maria Carluccio, Pietro Aleardo Siciliano and Alessandro Leone
Sensors 2025, 25(1), 266; https://doi.org/10.3390/s25010266 - 5 Jan 2025
Viewed by 1232
Abstract
In the medical field, there are several very different movement disorders, such as tremors, Parkinson’s disease, or Huntington’s disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in the modern era, the use of smart wrist devices, [...] Read more.
In the medical field, there are several very different movement disorders, such as tremors, Parkinson’s disease, or Huntington’s disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in the modern era, the use of smart wrist devices, such as smartwatches, wristbands, and smart bracelets is spreading among all categories of people. This diffusion is justified by the limited costs, ease of use, and less invasiveness (and consequently greater acceptability) than other types of sensors used for health status monitoring. This systematic review aims to synthesize research studies using smart wrist devices for a specific class of movement disorders. Following PRISMA-S guidelines, 130 studies were selected and analyzed. For each selected study, information is provided relating to the smartwatch/wristband/bracelet model used (whether it is commercial or not), the number of end-users involved in the experimentation stage, and finally the characteristics of the benchmark dataset possibly used for testing. Moreover, some articles also reported the type of raw data extracted from the smart wrist device, the implemented designed algorithmic pipeline, and the data classification methodology. It turned out that most of the studies have been published in the last ten years, showing a growing interest in the scientific community. The selected articles mainly investigate the relationship between smart wrist devices and Parkinson’s disease. Epilepsy and seizure detection are also research topics of interest, while there are few papers analyzing gait disorders, Huntington’s Disease, ataxia, or Tourette Syndrome. However, the results of this review highlight the difficulties still present in the use of the smartwatch/wristband/bracelet for the identified categories of movement disorders, despite the advantages these technologies could bring in the dissemination of low-cost solutions usable directly within living environments and without the need for caregivers or medical personnel. Full article
(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry)
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