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Machines
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Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction, 3rd Edition
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Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction, 3rd Edition

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 2552

Editors

Prof. Dr. Hongrui Cao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: dynamnic modeling; fault diagnosis of machinery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianping Xuan

E-Mail Website
Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: condition monitoring; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yongqiang Liu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Interests: condition monitoring; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue, titled “Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction (2nd Edition), ” (https://www.mdpi.com/journal/machines/special_issues/6632LFC6SB), we are pleased to announce the next in the series, entitled “Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction, 3rd Edition”.

A variety of industrial and household appliances are equipped with rotating systems. These are used in electric motors, pumps, rotary engines and compressors, turbines, automobiles, railways, the steel industry, power plants, material handling devices, jet engines, and many more. Bearings constitute one of the most critical components in rotating machinery. In today’s competitive environment, due to an increase in demand for running accuracy and nonlinearity involved in such systems, condition-based and predictive maintenance of bearings is gaining more popularity.

The objective of this Special Issue is to discover the most recent and significant developments in bearing modeling, fault diagnosis, and remaining useful life (RUL) prediction. This Special Issue encourages and welcomes original research articles that provide a significant contribution in the form of numerical, theoretical, or experimental analysis. Review articles related to these application areas are also invited.

Potential topics include, but are not limited to, the following:

  • Modelling and simulation;
  • Failure mechanisms analysis;
  • Intelligent sensor and flexible sensor;
  • Wireless sensors and sensor networks;
  • Signal processing theory and methods;
  • Data acquisition and measurement methods;
  • Bearing condition monitoring;
  • Machine learning and intelligent fault diagnosis;
  • Bearing RUL prediction;
  • Big data analytics in bearings;
  • Intelligent bearings.

Prof. Dr. Hongrui Cao
Prof. Dr. Jianping Xuan
Prof. Dr. Yongqiang Liu
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly 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 2400 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 modeling
  • failure analysis
  • sensing
  • signal processing
  • condition monitoring
  • fault diagnosis
  • RUL prediction
  • big data analytics

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Related Special Issue

Published Papers (2 papers)

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Research

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20 pages, 2021 KB  
Article
Noise-Conditioned Denoising Autoencoder with Temporal Attention for Bearing RUL Prediction
by Zhongtian Jin, Chong Chen, Aris Syntetos and Ying Liu
Machines 2026, 14(1), 75; https://doi.org/10.3390/machines14010075 - 8 Jan 2026
Cited by 1 | Viewed by 993
Abstract
Bearings are important elements of mechanical systems and the correct forecasting of their remaining useful life (RUL) is key to successful predictive maintenance. Nevertheless, noise interference during different operating conditions is also a significant problem in predicting their RUL. Existing denoising-based RUL prediction [...] Read more.
Bearings are important elements of mechanical systems and the correct forecasting of their remaining useful life (RUL) is key to successful predictive maintenance. Nevertheless, noise interference during different operating conditions is also a significant problem in predicting their RUL. Existing denoising-based RUL prediction models often show degraded performance when exposed to heterogeneous and non-stationary noise, resulting in unstable feature extraction and reduced generalisation. To address the challenge of heterogeneous and non-stationary noise in bearing RUL prediction, this study proposes a hybrid framework that combines a noise-conditioned convolutional denoising autoencoder (NC-CDAE) and a temporal attention transformer (TAT). The NC-CDAE adaptively suppresses diverse noise types through conditional modulation, while the TAT captures long-term temporal dependencies to enhance degradation trend learning. This synergistic design improves both the noise robustness and temporal modelling capability of the system. To further validate the model under varying conditions, synthetic datasets with different noise intensities were generated using a conditional generative adversarial network (cGAN). Comprehensive experiments show that the proposed NC-CDAE + TAT framework achieves lower and more stable errors than state-of-the-art methods, reducing RMSE by up to 23.6% and MAE by 18.2% on average and maintaining consistent performance (an RMSE between 0.155 and 0.194) across diverse conditions. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction, 3rd Edition)
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Review

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27 pages, 1990 KB  
Review
Remaining Useful Life Prediction for Engineering Applications: A Critical Review of Methodologies, Capability Gaps, and System-Level Integration
by Lin Wang, Yongmin Yang, Xu Luo and Mengqiao Chen
Machines 2026, 14(7), 724; https://doi.org/10.3390/machines14070724 - 26 Jun 2026
Viewed by 846
Abstract
As one of the core technologies of predictive maintenance, the development of remaining useful life (RUL) prediction is gradually transitioning from early single-mechanism modeling to a new phase characterized by the deep integration of physics-based approaches, data-driven methods, and uncertainty awareness. This paper [...] Read more.
As one of the core technologies of predictive maintenance, the development of remaining useful life (RUL) prediction is gradually transitioning from early single-mechanism modeling to a new phase characterized by the deep integration of physics-based approaches, data-driven methods, and uncertainty awareness. This paper first analyzes the fundamental challenges facing this development, such as multi-stress coupling, sensor degradation, and non-stationary noise. By comparing the core advantages and applicability boundaries of statistical models, data-driven models, and hybrid models, it constructs a capability map for RUL prediction. It further points out that current RUL prediction still faces critical capability gaps in areas such as physical consistency and uncertainty decoupling. Finally, the paper distills a new paradigm for engineering implementation, including mechanism-guided neural architecture design and digital twin-driven online parameter adaptation. The research indicates that future RUL prediction studies must transcend the competition over accuracy metrics and shift toward the coordinated development of robustness, interpretability, and decision adaptability—a trinity guided by the principles of “trustworthy AI.” Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction, 3rd Edition)
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