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
Multi-Sensor Fusion Technology for Feature Extraction and Intelligent Fault Diagnosis
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

Multi-Sensor Fusion Technology for Feature Extraction and Intelligent Fault Diagnosis

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

Deadline for manuscript submissions: 24 June 2026 | Viewed by 971

Special Issue Editors

Dr. Shengnan Tang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: fault perception and intelligent diagnosis-prediction methods for fluid machinery; health management and intelligent operation-m maintenance strategies for mechatronic-hydraulic equipment
Dr. Yong Zhu

E-Mail Website
Guest Editor
National Research Center of Pumps, Jiangsu University, Zhenjiang 212003, China
Interests: fluid machinery; intelligent control; signal processing; fault diagnosis; intelligent maintenance
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Gao

E-Mail Website
Guest Editor
National Research Center of Pumps, Jiangsu University, Zhenjiang 212003, China
Interests: electro-hydraulic hybrid drive and intelligent control strategies for mechanical equipment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multi-sensor fusion technology significantly enhances the accuracy and robustness of fault diagnosis by integrating heterogeneous sensor data to build a global perception model of environments or equipment. In fields such as industrial manufacturing and agricultural machinery, individual sensors are prone to interference from noise, occlusion, and other factors, while multi-source data fusion can compensate for information blind spots, enabling early fault warning and precise localization.

This Special Issue primarily includes, but is not limited to, research on signal processing methods and data fusion methods. Fusion technologies are mainly signal-level fusion, feature-level fusion, decision-level fusion, and hybrid fusion. Main applications include industrial equipment health management, intelligent monitoring of agricultural machinery, and perception systems for autonomous vehicles.

Dr. Shengnan Tang
Dr. Yong Zhu
Dr. Qiang Gao
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

  • feature extraction
  • fault diagnosis
  • multi-sensor fusion
  • signal-level fusion
  • feature-level fusion
  • decision-level fusion
  • hybrid fusion

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.

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

31 pages, 7054 KB  
Article
Few-Shot Fault Diagnosis of Railway Switch Machines Using Regularized Supervised Contrastive Meta-Learning
by Shanrong Li, Qingsheng Feng, Zhun Han, Shuai Xiao, Zhi Tao, Yafei Wang, Yiyang Zou and Hong Li
Sensors 2026, 26(9), 2827; https://doi.org/10.3390/s26092827 - 1 May 2026
Viewed by 325
Abstract
Railway switch machines are key devices in railway signal systems and have a critical impact on train operation safety. However, in real operating conditions, fault samples are scarce because field data collection is cumbersome and often constrained by safety requirements, which limits the [...] Read more.
Railway switch machines are key devices in railway signal systems and have a critical impact on train operation safety. However, in real operating conditions, fault samples are scarce because field data collection is cumbersome and often constrained by safety requirements, which limits the diagnostic accuracy and generalization capability of traditional fault diagnosis methods in few-shot scenarios. To address the challenge posed by insufficient accuracy in railway switch machine state recognition using sensors under few-shot conditions, we propose a regularized supervised contrastive meta-learning (RSCML) fault diagnosis method for switch machines. First, the tri-axial vibration signals acquired from the throwing rod and the reducer are transformed into axis-wise STFT spectrograms and organized as a unified three-channel time-frequency representation for subsequent cross-channel feature learning. Second, channel expansion and attention enhancement are employed to obtain more informative feature representations among similar fault types under limited samples. Finally, the feature extractor is integrated into the regularized supervised contrastive ANIL framework, while multi-loss optimization and stability regularization jointly constrain the meta-learning training process. Experimental results show that the proposed method achieves a maximum accuracy of 99.73% on 3-way and 5-way few-shot tasks, together with an F1-score of up to 99.72%. In the cross-category generalization experiment, it achieves a 93.08% accuracy and a 92.84% F1-score, indicating improved robustness when the fault categories at test time differ from those used during meta-training. The proposed method shows superior classification performance and stronger generalization to unseen fault categories under the current dataset setting, which suggests promising potential for switch machine fault diagnosis under limited sample conditions. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion Technology for Feature Extraction and Intelligent Fault Diagnosis)
23 pages, 5331 KB  
Article
A Temperature Compensation Method for the Bit Parameter Recorder in High-Temperature Deep Wells Based on Thermo-Mechanical Coupling
by Hengshuo Zhang, Zhenhuan Yi, Zhenbao Li, Yongyong Li and Yong Zhu
Sensors 2026, 26(6), 1884; https://doi.org/10.3390/s26061884 - 17 Mar 2026
Cited by 1 | Viewed by 335
Abstract
Measurement While Drilling (MWD) tools are widely employed in deep and ultra-deep well drilling. In the high-temperature and high-pressure (HTHP) environments characteristic of these wells, structural deformation induced by thermal expansion interferes with the bit parameter recorder’s sensor readings, thereby degrading the measurement [...] Read more.
Measurement While Drilling (MWD) tools are widely employed in deep and ultra-deep well drilling. In the high-temperature and high-pressure (HTHP) environments characteristic of these wells, structural deformation induced by thermal expansion interferes with the bit parameter recorder’s sensor readings, thereby degrading the measurement accuracy of weight on bit (WOB) and working torque (WT). To address this issue, this paper proposes a temperature compensation method based on thermo-mechanical coupling simulation. This method systematically establishes the quantitative relationships between multiple loads—including WT, WOB, temperature, and make-up torque—and the strain at critical locations of the bit parameter recorder through finite element analysis (FEA). Furthermore, surface calibration experiments have verified a strong linear correlation between the strain gauge voltage signals and the simulated strain. Building upon this foundation, an inversion-based compensation algorithm is developed. This algorithm effectively isolates the interference caused by thermally induced deformation and inversely deduces the true WOB and torque values by utilizing downhole-measured sensor voltage and temperature data. The research results demonstrate that the proposed temperature compensation method significantly improves the measurement accuracy of the bit parameter recorder under harsh, high-temperature operating conditions. The relative errors for both WOB and torque measurements are controlled to within 5%, providing a reliable solution for precise parameter measurement in high-temperature deep wells. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion Technology for Feature Extraction and Intelligent Fault Diagnosis)
►▼ 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