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
Fault Diagnosis Based on Sensing and Control Systems
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

Fault Diagnosis Based on Sensing and Control Systems

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 1555

Special Issue Editor

Dr. Chengwei Li

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: fault diagnosis; sensing and control

Special Issue Information

Dear Colleagues,

The advancements in sensor technologies have significantly enhanced the capabilities of fault diagnosis across various industries. These technologies enable the precise detection and analysis of system anomalies, leading to improved reliability and efficiency. The integration of sensors with advanced analytical methods offers substantial benefits in monitoring and controlling critical systems, ranging from machinery and vehicles to complex industrial processes.

This Special Issue aims to consolidate the latest research and developments in fault diagnosis based on sensing and control systems. We seek to explore innovative approaches and methodologies that leverage intelligent sensing, multisensor fusion, predictive maintenance, real-time fault detection, adaptive control systems, and the application of machine learning and artificial intelligence in fault diagnosis.

We invite contributions that delve into theoretical frameworks, computational models, experimental studies, and practical implementations across various engineering domains. We encourage submissions on a broad range of issues, including, but not limited to, the following:

  • Intelligent sensing;
  • Multisensor fusion;
  • Predictive maintenance;
  • Real-time fault detection;
  • Adaptive control systems;
  • Machine learning and artificial intelligence in fault diagnosis;
  • Sensor-based monitoring systems.

By showcasing cutting-edge research in these areas, this Special Issue aims to foster academic exchange and drive advancements in the field of sensor-based fault diagnosis and control systems.

Dr. Chengwei Li
Guest Editor

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 sensing
  • multisensor fusion
  • predictive maintenance
  • real-time fault detection
  • adaptive control systems
  • machine learning and artificial intelligence in fault diagnosis
  • sensor-based monitoring systems

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 policies can be found here.

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

20 pages, 10227 KiB  
Article
A Novel Rolling Bearing Fault Diagnosis Method Based on the NEITD-ADTL-JS Algorithm
by Shi Zhuo, Xiaofeng Bai, Junlong Han, Jianpeng Ma, Bojun Sun, Chengwei Li and Liwei Zhan
Sensors 2025, 25(3), 873; https://doi.org/10.3390/s25030873 - 31 Jan 2025
Viewed by 599
Abstract
This paper proposes an innovative bearing fault diagnosis method aimed at enhancing the accuracy and effectiveness of transfer learning. The innovation lies in the signal preprocessing stage, where a Noise Eliminated Intrinsic Time-Scale Decomposition (NEITD) algorithm is introduced. This algorithm adaptively decomposes unified-phase [...] Read more.
This paper proposes an innovative bearing fault diagnosis method aimed at enhancing the accuracy and effectiveness of transfer learning. The innovation lies in the signal preprocessing stage, where a Noise Eliminated Intrinsic Time-Scale Decomposition (NEITD) algorithm is introduced. This algorithm adaptively decomposes unified-phase sine wave signals to effectively extract the geometric mean of the intrinsic rotational component, and selects the optimal decomposition result based on the orthogonality index, significantly improving the quality and reliability of the signals. In addition, fault diagnosis parameters are adaptively optimized using an improved adaptive deep transfer learning (ADTL) network combined with the Jellyfish Search (JS) algorithm, further enhancing diagnostic performance. By innovatively combining signal noise reduction, feature extraction, and deep learning optimization techniques, this method significantly improves fault diagnosis accuracy and robustness. Comparative simulations and experimental analyses show that the NEITD algorithm outperforms traditional methods in both signal decomposition performance and diagnostic accuracy. Furthermore, the NEITD-ADTL-JS method demonstrates stronger sensitivity and recognition capabilities across various fault types, achieving a 5.29% improvement in accuracy. Full article
(This article belongs to the Special Issue Fault Diagnosis Based on Sensing and Control Systems)
►▼ Show Figures

Figure 1

15 pages, 547 KiB  
Article
A Novel Ultra-High Voltage Direct Current Line Fault Diagnosis Method Based on Principal Component Analysis and Kernel Density Estimation
by Haojie Zhang and Qingwu Gong
Sensors 2025, 25(3), 642; https://doi.org/10.3390/s25030642 - 22 Jan 2025
Viewed by 573
Abstract
As renewable energy resources are increasingly deployed on a large scale in remote areas, their share within the power grid continues to expand, rendering direct current (DC) transmission essential to the stability and efficiency of power systems. However, existing transmission line protection principles [...] Read more.
As renewable energy resources are increasingly deployed on a large scale in remote areas, their share within the power grid continues to expand, rendering direct current (DC) transmission essential to the stability and efficiency of power systems. However, existing transmission line protection principles are constrained by limited fault feature quantities and insufficient correlation exploration among features, leading to operational refusals under remote and high-resistance fault conditions. To address these limitations in traditional protection methods, this study proposes an innovative single-ended protection principle based on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE). Initially, PCA is employed for multidimensional feature extraction from fault data, followed by KDE to construct a joint probability density function of the multidimensional fault features, allowing for fault type identification based on the joint probability density values of new samples. In comparison to conventional methods, the proposed approach effectively uncovers intrinsic correlations among multidimensional features, integrating them into a comprehensive feature set for fault diagnosis. Simulation results indicate that the method exhibits robustness across various transition resistances and fault distances, demonstrates insensitivity to sampling frequency, and achieves 100% accuracy in fault identification across sampling time windows of 0.5 ms, 1 ms, and 2 ms. Full article
(This article belongs to the Special Issue Fault Diagnosis Based on Sensing and Control Systems)
►▼ 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