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Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for...
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Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for Industrial PHM

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1127

Special Issue Editors

Dr. Diwang Ruan

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
Interests: diagnosis and prognosis; embedded-interpretable AI; signal processing
Prof. Dr. Jianping Yan

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
Interests: gas turbine aerodynamics and heat transfer; computational fluid dynamics; AI-driven system modeling, optimization and control; prognostics and health management
Dr. Mengdi Li

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Tsinghua University, Beijing, China
Interests: weak signal detection; mechanical fault diagnosis.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junxing Li

E-Mail Website
Guest Editor
School of Mechatronical Engineering, Henan University of Science and Technology, Luoyang, China
Interests: RUL prediction; fault diagnosis; reliability design and evaluation of rolling bearings

Special Issue Information

Dear Colleagues,

Prognostics and health management (PHM) is crucial for industrial safety and sustainability. This Special Issue seeks cutting-edge data-driven approaches for remaining useful life (RUL) prediction, addressing critical challenges including cross-domain generalization, long-term degradation extrapolation, uncertainty quantification, noise-robust multi-sensor fusion, and edge-deployable lightweight design. We invite original research leveraging emerging methodologies, such as generative trajectory modeling, physics-informed neural networks (PINNs), LLM-based transfer learning, self-data-driven paradigms, and digital twin-enabled degradation simulation. Submissions must demonstrate significant advances in prediction accuracy, robustness, or interpretability, validated in industrial applications (manufacturing, aerospace, energy, transportation, etc.). This Special Issue aims to bridge theoretical innovation with engineering reliability, fostering next-generation predictive maintenance frameworks.

Dr. Diwang Ruan
Prof. Dr. Jianping Yan
Dr. Mengdi Li
Prof. Dr. Junxing Li
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. 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

  • prognostics and health management (PHM)
  • remaining useful life (RUL) prediction
  • data-driven approaches
  • cross-domain generalization
  • long-term prediction
  • uncertainty quantification
  • physics-informed neural networks (PINNs)
  • LLM-based transfer learning
  • industrial applications

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Published Papers (2 papers)

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Research

19 pages, 9059 KB  
Article
Data–Model Integration-Driven Temperature Rise Prediction Method for New Energy Electric Drive Bearings
by Fang Yang, Xi Chen, Zhidan Zhong, Jun Ye and Weiqi Zhang
Machines 2025, 13(10), 925; https://doi.org/10.3390/machines13100925 - 7 Oct 2025
Viewed by 280
Abstract
Accurate prediction of bearing temperature rise offers essential support for equipment operation and optimized design. However, traditional methods often lack accuracy under the complex operating conditions of new energy electric drive bearings. To address this, we propose a model–data integration-driven approach for predicting [...] Read more.
Accurate prediction of bearing temperature rise offers essential support for equipment operation and optimized design. However, traditional methods often lack accuracy under the complex operating conditions of new energy electric drive bearings. To address this, we propose a model–data integration-driven approach for predicting the temperature rise in new energy electric drive bearings. First, a data-driven optimization method is employed to integrate mathematical and simulation models, generating highly reliable simulation data. Then, the simulation data and measured data are fused to construct an integrated dataset for bearing temperature rise. Finally, a CNN-LSTM prediction model is established and trained using this dataset. Validation experiments were carried out on the EV6206E-2RZTN/C3 bearing to verify the effectiveness of the proposed method. Results show (1) under constant operating conditions, the MAE during the temperature rise phase is 0.773 °C, and the steady-state phase maximum MAE is 0.686 °C, and (2) under variable operating conditions, the maximum MAE during the temperature rise phase is 0.713 °C, and the steady-state phase maximum MAE is 0.764 °C. The proposed method achieves effective prediction of temperature rise in electric drive bearings and offers a valuable reference for addressing temperature prediction challenges under complex operational conditions. Full article
(This article belongs to the Special Issue Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for Industrial PHM)
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24 pages, 14760 KB  
Article
Remaining Useful Life Prediction of Electric Drive Bearings in New Energy Vehicles: Based on Degradation Assessment and Spatiotemporal Feature Fusion
by Fang Yang, En Dong, Zhidan Zhong, Weiqi Zhang, Yunhao Cui and Jun Ye
Machines 2025, 13(10), 914; https://doi.org/10.3390/machines13100914 - 3 Oct 2025
Cited by 1 | Viewed by 326
Abstract
Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, [...] Read more.
Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, complicating the modeling of time dependent relationships and degradation states; therefore, a piecewise linear degradation model is appropriate. An RUL prediction method is proposed based on degradation assessment and spatiotemporal feature fusion, which extracts strongly time correlated features from bearing vibration data, evaluates sensitive indicators, constructs weighted fused degradation features, and identifies abrupt degradation points. On this basis, a piecewise linear degradation model is constructed that uses a path graph structure to represent temporal dependencies and a temporal observation window to embed temporal features. By incorporating GAT-LSTM, RUL prediction for bearings is performed. The method is validated on the XJTU-SY dataset and on a loaded ball bearing test rig for electric vehicle drive motors, yielding comprehensive vibration measurements for life prediction. The results show that the method captures deep degradation information across the full bearing life cycle and delivers accurate, robust predictions, providing guidance for the health assessment of electric drive bearings in new energy vehicles. Full article
(This article belongs to the Special Issue Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for Industrial PHM)
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