sensors-logo

Journal Browser

Journal Browser

Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1406

Special Issue Editors


E-Mail Website
Guest Editor
Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
Interests: fault detection and diagnosis; signal processing; multiscale signal analysis; sensor fusion; signal to image conversion and analysis; artificial intelligence; explainable machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bearings are fundamental components in rotating machinery, supporting motion and reducing friction between moving parts. They are widely used in industries such as manufacturing, transportation, and aerospace. As machinery becomes more advanced and operates under increasingly demanding and harsh conditions, bearings are subjected to high loads, speeds, and prolonged operation. These factors contribute to wear and the development of incipient faults, which, if undetected, can lead to severe consequences, including machine failure, downtime, economic loss, and safety hazards. Therefore, accurate and early diagnosis of bearing faults is essential for improving equipment reliability, optimizing maintenance, and ensuring operational safety.

This Special Issue focuses on recent advances in bearing fault diagnosis using both single-sensor techniques and multi-sensor fusion approaches. Single sensor systems are favored for their simplicity and low cost, while sensor fusion strategies enhance fault detection and classification by integrating data from multiple sensing modalities such as vibration, acoustic emission, and current signals, etc.

This Special Issue invites original research articles and reviews that explore novel methodologies, signal processing techniques, machine learning models, deep learning architectures, and practical applications related to both single-sensor and sensor fusion-based bearing fault diagnosis. Furthermore, research concerning recent advances in sensor fault diagnosis is also encouraged.

Prof. Dr. Jong-Myon Kim
Dr. Zahoor Ahmad
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

  • bearing fault diagnosis
  • single sensor techniques
  • sensor fusion
  • condition monitoring
  • machine learning

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)

Order results
Result details
Select all
Export citation of selected articles as:

Research

28 pages, 7790 KB  
Article
A Hybrid Deep Learning Framework for Fault Diagnosis in Milling Machines
by Muhammad Farooq Siddique, Wasim Zaman, Muhammad Umar, Jae-Young Kim and Jong-Myon Kim
Sensors 2025, 25(18), 5866; https://doi.org/10.3390/s25185866 - 19 Sep 2025
Viewed by 469
Abstract
This paper presents a hybrid fault-diagnosis framework for milling cutting tools designed to address three persistent challenges in industrial monitoring: noisy vibration signals, limited fault labels, and variability across operating conditions. The framework begins by removing baseline drift from raw signals to improve [...] Read more.
This paper presents a hybrid fault-diagnosis framework for milling cutting tools designed to address three persistent challenges in industrial monitoring: noisy vibration signals, limited fault labels, and variability across operating conditions. The framework begins by removing baseline drift from raw signals to improve the signal-to-noise ratio. Logarithmic continuous wavelet scalograms are then constructed to provide precise time-frequency localization and reveal fault-related harmonics. To enhance feature clarity, a Canny edge operator is applied, suppressing minor artifacts and reducing intra-class variation so that key diagnostic structures are emphasized. Feature representation is obtained through a dual-branch encoder, where one pathway captures localized patterns while the other preserves long-range dependencies, resulting in compact and discriminative fault descriptors. These descriptors are integrated by an ensemble decision mechanism that assigns validation-guided weights to individual learners, ensuring reliable fault identification, improved robustness under noise, and stable performance across diverse operating conditions. Experimental validation on real-world cutting tool data demonstrates an accuracy of 99.78%, strong resilience to environmental noise, and consistent diagnostic performance under variable conditions. The framework remains lightweight, scalable, and readily deployable, providing a practical solution for high-precision tool fault diagnosis in data-constrained industrial environments. Full article
Show Figures

Figure 1

24 pages, 4430 KB  
Article
Early Bearing Fault Diagnosis in PMSMs Based on HO-VMD and Weighted Evidence Fusion of Current–Vibration Signals
by Xianwu He, Xuhui Liu, Cheng Lin, Minjie Fu, Jiajin Wang and Jian Zhang
Sensors 2025, 25(15), 4591; https://doi.org/10.3390/s25154591 - 24 Jul 2025
Cited by 1 | Viewed by 652
Abstract
To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper [...] Read more.
To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article
Show Figures

Figure 1

Back to TopTop