Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Intelligent Sensors and Signal Processing in Industry—2nd Edition
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Sensors and Signal Processing in Industry—2nd Edition

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1348

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, Beijing 100044, China
Interests: ultrasonic non-destructive testing; rail transit; intelligent sensing
Special Issues, Collections and Topics in MDPI journals
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 Issues, Collections and Topics in MDPI journals

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 250 words) can be sent to the Editorial Office for assessment.

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)

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 13510 KB  
Article
A Nonlinear Error Compensation Method for Heterodyne Interferometry Based on Self-Supervised Physics-Informed Neural Networks with Frequency-Domain Priors
by Yao Wang, Hongyu Sun, Jiakun Li, Chenlong Ma, Ying Zhang, Xiao Wang and Qibo Feng
Sensors 2026, 26(10), 3000; https://doi.org/10.3390/s26103000 - 10 May 2026
Viewed by 259
Abstract
Although laser heterodyne interferometric sensing systems offer exceptional theoretical resolution, practical precision is constrained by intractable nonlinear errors stemming from optical imperfections. Conventional compensation methods suffer from hardware dependency, complexity, and performance degradation under low signal-to-noise ratios (SNR). To address this, we propose [...] Read more.
Although laser heterodyne interferometric sensing systems offer exceptional theoretical resolution, practical precision is constrained by intractable nonlinear errors stemming from optical imperfections. Conventional compensation methods suffer from hardware dependency, complexity, and performance degradation under low signal-to-noise ratios (SNR). To address this, we propose a precision calibration method using a self-supervised Physics-Informed Neural Network (PINN) guided by frequency-domain priors with harmonic distribution characteristics. This approach establishes a robust compensation model by inverting equivalent parameter sets and error curves in a single step. Specifically, leveraging high-precision displacement references, the method extracts measurement residuals containing periodic physical features. Subsequently, it integrates frequency-domain priors into a physically constrained network architecture: theoretical frequency characteristics construct masks to generate high-confidence pseudo-labels, while the error equation is recast as a differentiable physical layer imposing explicit hard constraints during forward propagation. This mechanism enables precise identification of the system’s nonlinear physical properties against high background noise. Experimental results show that the root-mean-square (RMS) value of the nonlinear error was reduced from 1.90 nm to 0.23 nm, with a compensation rate reaching up to 88.13%. This method provides a reliable framework for the intelligent calibration and error self-characterization of heterodyne interferometric industrial sensors in the field of precision metrology sensors. Full article
(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry—2nd Edition)
►▼ Show Figures

Figure 1

16 pages, 4163 KB  
Article
Methods for Improving the Straightness Accuracy of Laser Fiber-Based Collimation Measurement
by Ying Zhang, Peizhi Jia, Qibo Feng, Fajia Zheng, Fei Long, Chenlong Ma and Lili Yang
Sensors 2026, 26(9), 2676; https://doi.org/10.3390/s26092676 - 25 Apr 2026
Viewed by 892
Abstract
Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of [...] Read more.
Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes. Full article
(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry—2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop