Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics
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 for Condition Monitoring, Diagnosis, and Prognostics

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2733

Special Issue Editors

Dr. Tianyu Gao

E-Mail Website
Guest Editor
Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China
Interests: fault diagnosis; condition monitoring; machine learning; deep learning; interpretability
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 Website
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu, China
Interests: anomaly detection; fault diagnosis; deep learning; transfer learning; electromechanical equipment
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 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) 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, transportation, healthcare, 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 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

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

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (4 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

Jump to: Review

14 pages, 15017 KiB  
Article
Maintenance Decision-Making Using Intelligent Prognostics Within a Single Spare Parts Support System
by Bowei Zhang, Changhua Hu, Jianfei Zheng and Hong Pei
Sensors 2025, 25(3), 837; https://doi.org/10.3390/s25030837 - 30 Jan 2025
Viewed by 304
Abstract
Health management is the foothold of remaining useful life (RUL) prediction, known as ‘prognostics’. However, sudden failures in complex systems can lead to increased downtime and maintenance costs, ultimately diminishing system health and availability. Considering intelligent prognostics of components, maintenance decision-making for spare [...] Read more.
Health management is the foothold of remaining useful life (RUL) prediction, known as ‘prognostics’. However, sudden failures in complex systems can lead to increased downtime and maintenance costs, ultimately diminishing system health and availability. Considering intelligent prognostics of components, maintenance decision-making for spare parts ordering and replacement is proposed within a spare parts support system. The decision-making process aims to minimize costs while maximizing availability as its primary objective. It considers spare parts ordering time and replacement time as key decision variables. By developing a maintenance decision-making model, it aims to determine the optimal time for ordering and replacing spare parts. This maintenance approach is designed to provide technical support for effective and rational equipment management decision-making. Full article
(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)
►▼ Show Figures

Figure 1

26 pages, 13903 KiB  
Article
Triboelectric Nanogenerator-Embedded Intelligent Self-Aligning Roller Bearing with the Capability of Self-Sensing, Monitoring, and Fault Diagnosis
by Hao Shen, Yufan Lv, Yun Kong, Qinkai Han, Ke Chen, Zhibo Geng, Mingming Dong and Fulei Chu
Sensors 2024, 24(23), 7618; https://doi.org/10.3390/s24237618 - 28 Nov 2024
Viewed by 532
Abstract
Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has [...] Read more.
Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has an application in fault diagnosis. The designed I-SAB is compactly embedded with a novel sweep-type triboelectric nanogenerator (TENG). The TENG is realized within the proposed I-SAB using a comb–finger electrode pair and a flannelette triboelectric layer. A floating, sweeping, and freestanding mode is utilized, which can prevent collisions and considerably enhance the operational life of the embedded TENG. Experiments are subsequently conducted to optimize the output performance and sensing sensitivity of the proposed I-SAB. The results of a speed-sensing experiment show that the characteristic frequencies of triboelectric current and voltage signals are both perfectly proportional to the rotational speed, indicating that the designed I-SAB has the self-sensing capability for rotational speed. Additionally, as both the bias angle and rotational speed of the SAB increase, the envelope amplitudes of the triboelectric voltage signals generated by the I-SAB rise at a rate of 0.0057 V·deg−1·rpm−1. To further demonstrate the effectiveness of the triboelectric signals emitted from the designed I-SAB in terms of self-powered fault diagnosis, a Multi-Scale Discrimination Network (MSDN), based on the ResNet18 architecture, is proposed in order to classify the various fault conditions of the SAB. Using the triboelectric voltage and current signals emitted from the designed I-SAB as inputs, the proposed MSDN model yields excellent average diagnosis accuracies of 99.8% and 99.1%, respectively, indicating its potential for self-powered fault diagnosis. Full article
(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)
►▼ Show Figures

Figure 1

12 pages, 3089 KiB  
Article
Effect of Dielectric Layer on Miniaturized Patch Antenna Sensor
by Caifeng Chen, Lei Zou, Chenglong Bi and Andong Wang
Sensors 2024, 24(23), 7608; https://doi.org/10.3390/s24237608 - 28 Nov 2024
Viewed by 717
Abstract
Miniature patch antenna sensors have great potential in the field of structural health monitoring for crack propagation detection due to their small size and high sensitivity. A primary research focus has been achieving efficient miniaturization, with the performance of the dielectric layer playing [...] Read more.
Miniature patch antenna sensors have great potential in the field of structural health monitoring for crack propagation detection due to their small size and high sensitivity. A primary research focus has been achieving efficient miniaturization, with the performance of the dielectric layer playing a pivotal role. Studies have demonstrated that increasing the relative dielectric constant (εr) of the dielectric layer can reduce antenna size, but higher dielectric losses (tanδ) can lower radiation efficiency. This study identifies the optimal dielectric properties by examining the interplay between εr and tanδ to balance size reduction and radiation efficiency. Additionally, while increasing the dielectric layer’s thickness improves bandwidth and radiation efficiency, a thinner layer is preferred to maintain overall performance without compromising radiation efficiency. Furthermore, the resonant frequency of the smaller-sized patch antenna sensor exhibits greater detection sensitivity to crack propagation. These insights provide useful guidance for selecting effective dielectric layers and assist in the miniaturization design of antenna sensors. Full article
(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)
►▼ Show Figures

Figure 1

Review

Jump to: Research

41 pages, 10236 KiB  
Review
Coaxial Cable Distributed Strain Sensing: Methods, Applications and Challenges
by Stephanie King, Gbanaibolou Jombo, Oluyomi Simpson, Wenbo Duan and Adrian Bowles
Sensors 2025, 25(3), 650; https://doi.org/10.3390/s25030650 - 22 Jan 2025
Viewed by 490
Abstract
Distributed strain sensing is a powerful tool for in situ structural health monitoring for a wide range of critical engineering infrastructures. Strain information from a single sensing device can be captured from multiple locations simultaneously, offering a reduction in hardware, wiring, installation costs, [...] Read more.
Distributed strain sensing is a powerful tool for in situ structural health monitoring for a wide range of critical engineering infrastructures. Strain information from a single sensing device can be captured from multiple locations simultaneously, offering a reduction in hardware, wiring, installation costs, and signal analysis complexity. Fiber optic distributed strain sensors have been the widely adopted approach in this field, but their use is limited to lower strain applications due to the fragile nature of silica fiber. Coaxial cable sensors offer a robust structure that can be adapted into a distributed strain sensor. They can withstand greater strain events and offer greater resilience in harsh environments. This paper presents the developments in methodology for coaxial cable distributed strain sensors. It explores the two main approaches of coaxial cable distributed strain sensing such as time domain reflectometry and frequency domain reflectometry with applications. Furthermore, this paper highlights further areas of research challenges in this field, such as the deconvolution of strain and temperature effects from coaxial cable distributed strain sensor measurements, mitigating the effect of dielectric permittivity on the accuracy of strain measurements, addressing manufacturing challenges with the partial reflectors for a robust coaxial cable sensor, and the adoption of data-driven analysis techniques for interrogating the interferogram to eliminate concomitant measurement effects with respect to temperature, dielectric permittivity, and signal-to-noise ratio, amongst others Full article
(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop