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Intelligent Sensors and Signal Processing in Industry
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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 2593

Special Issue Editors

Dr. Lisha Peng

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Oleksii Karpenko

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Hongyu Sun

E-Mail Website
Guest Editor
School of Physical Science and Engineering, Beijing Jiaotong University, 100044, China
Interests: ultrasonic non-destructive testing; rail transit; intelligent sensing
Dr. Zhichao Cai

E-Mail Website
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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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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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 372
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 1252
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 383
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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