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Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics, 2nd Edition
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Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics, 2nd Edition

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1640

Special Issue Editors

Dr. Tianyu Gao

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Guest Editor
School of Electronics and Information, Harbin Institute of Technology, Harbin 150001, China
Interests: fault diagnosis; condition monitoring; intelligent sensing; artificial intelligence; analog circuits; mechanical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yinsheng Chen

E-Mail Website
Guest Editor
School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
Interests: machine olfaction; electronic noise; sensor fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Jianyu Wang

E-Mail
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu, China
Interests: anomaly detection; fault diagnosis; deep learning; transfer learning; electromechanical equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoli Zhao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
Interests: fault diagnosis; health management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the advancements and applications of intelligent sensors for condition monitoring, diagnosis, and prognostics. We seek original research papers that showcase the latest sensor technologies and intelligent algorithms aimed at enhancing real-time monitoring, precise diagnostics, and accurate prognosis of machine health. Contributions should focus on the exploration on the integration of advanced analytics, including AI and ML, with intelligent sensors to improve the accuracy and speed of data processing and decision-making.

We are particularly interested in papers that demonstrate how intelligent sensors enable real-time decision-making and improve operational policies. Research addressing both electro-mechanical systems (e.g., rotational machinery, Joint Fault Prediction for Collaborative Robots) and emerging systems (e.g., renewable energy) is encouraged. Moreover, papers that showcase the application of intelligent sensors in diverse industries and contexts, including but not limited to manufacturing (such as industrial robot status monitoring), transportation, healthcare (such as the precise operation of the surgical robot), Service robot environmental perception and environmental monitoring, are also encouraged.

Overall, we aim to bring together a collection of high-quality research papers that exhibit the latest advancements and applications of intelligent sensors in condition monitoring, diagnosis, and prognostics, and their potential to revolutionize various industries and improve our daily lives.

Dr. Tianyu Gao
Dr. Yinsheng Chen
Dr. Jianyu Wang
Dr. Xiaoli Zhao
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

  • intelligent sensors
  • measurement condition monitoring
  • anomaly detection
  • fault diagnosis prognostics
  • machine learning
  • deep learning
  • signal processing

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Related Special Issue

Published Papers (3 papers)

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Research

19 pages, 6282 KB  
Article
Effect of Sensor Head Orientation on the Accuracy of Magnetic Defect Detection in Steel-Cord Conveyor Belts
by Aleksandra Rzeszowska and Ryszard Błażej
Sensors 2025, 25(23), 7364; https://doi.org/10.3390/s25237364 - 3 Dec 2025
Viewed by 234
Abstract
This study analyses how the orientation of the measurement head in a magnetic diagnostic system affects the parameters of magnetic signals recorded during steel-cord conveyor belt inspection. The experiments were conducted on a laboratory test stand using a reference belt with artificial defects [...] Read more.
This study analyses how the orientation of the measurement head in a magnetic diagnostic system affects the parameters of magnetic signals recorded during steel-cord conveyor belt inspection. The experiments were conducted on a laboratory test stand using a reference belt with artificial defects at two belt speeds and several sensitivity thresholds. Three types of head rotation were analyzed: longitudinal (OX), transverse (OY), and planar (OZ). For each configuration, a set of geometric signal parameters was calculated, including length, width, orientation, eccentricity, and solidity. The results showed that rotation about the OX axis caused the greatest geometric distortions (increased orientation_deg and eccentricity). Rotation about the OY axis produced amplitude asymmetry and changes in solidity (circularity), while rotation about the OZ axis resulted in twisting and displacement of the signal centroid. The total area (area_mm2) remained stable, confirming the geometric nature of the observed changes. Even small head deviations (5–10°) may introduce significant interpretation errors. Therefore, the application of geometric calibration and orientation compensation algorithms is recommended to improve the online diagnostic accuracy of the measurement system. Full article
(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics, 2nd Edition)
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20 pages, 2158 KB  
Article
High-Precision Coal Mine Microseismic P-Wave Arrival Picking via Physics-Constrained Deep Learning
by Kai Qin, Zhigang Deng, Xiaohan Li, Zewei Lian and Jinjiao Ye
Sensors 2025, 25(23), 7103; https://doi.org/10.3390/s25237103 - 21 Nov 2025
Viewed by 366
Abstract
The automatic identification of P-wave arrival times in microseismic signals is crucial for the intelligent monitoring and early warning of dynamic hazards in coal mines. Traditional methods suffer from low accuracy and poor stability due to complex underground geological conditions and substantial noise [...] Read more.
The automatic identification of P-wave arrival times in microseismic signals is crucial for the intelligent monitoring and early warning of dynamic hazards in coal mines. Traditional methods suffer from low accuracy and poor stability due to complex underground geological conditions and substantial noise interference. This paper proposes a microseismic P-wave arrival time automatic picking model that integrates physical constraints with a deep learning architecture. This study trained and optimized the model using a high-quality, manually labeled dataset. A systematic comparison with the AR picker algorithm and the short-term–long-term average ratio method revealed that this model achieved a precision of 96.60%, a recall of 90.59%, and an F1 score of 93.50% on the test set, with a P-wave arrival time-picking error of less than 20 ms. The average arrival time error was only 5.49 ms, significantly outperforming traditional methods. In cross-mining area generalization tests, the model performed excellently in two mining areas with consistent sampling frequencies (1000 Hz) and high signal-to-noise ratios, demonstrating good engineering transferability. However, its performance decreased in a mining area with a higher sampling rate and stronger noise, indicating its sensitivity to data acquisition parameters. This study developed a high-precision, robust, and potentially cross-domain adaptive model for automatically picking microseismic P-wave arrival times. This model provides support for the automation, precision, and intelligence of coal mine microseismic monitoring systems and has significant practical value in promoting real-time early warning and risk prevention for mine dynamic hazards. Full article
(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics, 2nd Edition)
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18 pages, 4036 KB  
Article
Precise Control of Micropipette Flow Rate for Fluorescence Imaging in In Vivo Micromanipulation
by Ruimin Li, Shaojie Fu, Zijian Guo, Jinyu Qiu, Yuzhu Liu, Mengya Liu, Qili Zhao and Xin Zhao
Sensors 2025, 25(21), 6647; https://doi.org/10.3390/s25216647 - 30 Oct 2025
Viewed by 826
Abstract
Precise regulation of micropipette outlet flow is critical for fluorescence imaging in vivo micromanipulations. In such procedures, a micropipette with a micro-sized opening is driven by gas pressure to deliver internal solution into the in vivo environment. The outlet flow rate needs to [...] Read more.
Precise regulation of micropipette outlet flow is critical for fluorescence imaging in vivo micromanipulations. In such procedures, a micropipette with a micro-sized opening is driven by gas pressure to deliver internal solution into the in vivo environment. The outlet flow rate needs to be precisely regulated to ensure a uniform and stable fluorescence distribution. However, conventional manual pressure injection methods face inherent limitations, including insufficient precision and poor reproducibility. Existing commercial microinjection systems lack a quantitative relationship between pressure and flow rate. And existing calibration methods in the field of microfluidics suffer from a limited flow-rate measurement resolution, constraining the establishment of a precise pressure–flow quantitative relationship. To address these challenges, we developed a closed-loop pressure regulation system with 1 Pa-level control resolution and established a quantitative calibration of the pressure–flow relationship using a droplet-based method. The calibration revealed a linear relationship with a mean pressure–flow gain of 4.846 × 10−17m3·s−1·Pa−1 (R2 > 0.99). Validation results demonstrated that the system achieved the target outlet flow rate with a flow control error less than 10 fL/s. Finally, the application results in brain-slice environment confirmed its capability to maintain stable fluorescence imaging, with fluorescence intensity fluctuations around 1.3%. These results demonstrated that the proposed approach provides stable, precise, and reproducible flow regulation under physiologically relevant conditions, thereby offering a valuable tool for in vivo micromanipulation and detection. Full article
(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics, 2nd Edition)
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