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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: closed (28 February 2026) | Viewed by 6578

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 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. 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 (7 papers)

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Research

21 pages, 1661 KB  
Article
Effects of Window and Batch Size on Autoencoder-LSTM Models for Remaining Useful Life Prediction
by Eugene Jeon, Donghwan Jin and Yeonhee Kim
Machines 2026, 14(2), 135; https://doi.org/10.3390/machines14020135 - 23 Jan 2026
Viewed by 731
Abstract
Remaining useful life (RUL) prediction is central to predictive maintenance, but acquiring sufficient run-to-failure data remains challenging. To better exploit limited labeled data, this study investigates a pipeline combining an unsupervised autoencoder (AE) and supervised LSTM regression on the NASA C-MAPSS dataset. Building [...] Read more.
Remaining useful life (RUL) prediction is central to predictive maintenance, but acquiring sufficient run-to-failure data remains challenging. To better exploit limited labeled data, this study investigates a pipeline combining an unsupervised autoencoder (AE) and supervised LSTM regression on the NASA C-MAPSS dataset. Building on an AE-LSTM baseline, we analyze how window size and batch size affect accuracy and training efficiency. Using the FD001 and FD004 subsets with training-capped RUL labels, we perform multi-seed experiments over a wide grid of window lengths and batch sizes. The AE is pre-trained on normalized sensor streams and reused as a feature extractor, while the LSTM head is trained with early stopping. Performance was assessed using RMSE, C-MAPSS score, and training time, reporting 95% confidence intervals. Results show that fine-tuning the encoder with a batch size of 128 yielded the best mean RMSE of 13.99 (FD001) and 28.67 (FD004). We obtained stable optimal window ranges (40–70 for FD001; 60–80 for FD004) and found that batch sizes of 64–256 offer the best accuracy–efficiency trade-off. These optimal ranges were further validated using Particle Swarm Optimization (PSO). These findings offer practical recommendations for tuning AE-LSTM-based RUL prediction models and demonstrate that performance remains stable within specific hyperparameter ranges. 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, 5571 KB  
Article
Bearing Fault Diagnosis Based on a Depthwise Separable Atrous Convolution and ASPP Hybrid Network
by Xiaojiao Gu, Chuanyu Liu, Jinghua Li, Xiaolin Yu and Yang Tian
Machines 2026, 14(1), 93; https://doi.org/10.3390/machines14010093 - 13 Jan 2026
Viewed by 465
Abstract
To address the computational redundancy, inadequate multi-scale feature capture, and poor noise robustness of traditional deep networks used for bearing vibration and acoustic signal feature extraction, this paper proposes a fault diagnosis method based on Depthwise Separable Atrous Convolution (DSAC) and Acoustic Spatial [...] Read more.
To address the computational redundancy, inadequate multi-scale feature capture, and poor noise robustness of traditional deep networks used for bearing vibration and acoustic signal feature extraction, this paper proposes a fault diagnosis method based on Depthwise Separable Atrous Convolution (DSAC) and Acoustic Spatial Pyramid Pooling (ASPP). First, the Continuous Wavelet Transform (CWT) is applied to the vibration and acoustic signals to convert them into time–frequency representations. The vibration CWT is then fed into a multi-scale feature extraction module to obtain preliminary vibration features, whereas the acoustic CWT is processed by a Deep Residual Shrinkage Network (DRSN). The two feature streams are concatenated in a feature fusion module and subsequently fed into the DSAC and ASPP modules, which together expand the effective receptive field and aggregate multi-scale contextual information. Finally, global pooling followed by a classifier outputs the bearing fault category, enabling high-precision bearing fault identification. Experimental results show that, under both clean data and multiple low signal-to-noise ratio (SNR) noise conditions, the proposed DSAC-ASPP method achieves higher accuracy and lower variance than baselines such as ResNet, VGG, and MobileNet, while requiring fewer parameters and FLOPs and exhibiting superior robustness and deployability. 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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21 pages, 15751 KB  
Article
Fault Diagnosis of Gearbox Bearings Based on Multi-Feature Fusion Dual-Channel CNN-Transformer-CAM
by Lihai Chen, Yonghui He, Ao Tan, Xiaolong Bai, Zhenshui Li and Xiaoqiang Wang
Machines 2026, 14(1), 92; https://doi.org/10.3390/machines14010092 - 13 Jan 2026
Viewed by 795
Abstract
As a core component of the gearbox, bearings are crucial to the stability and reliability of the transmission system. However, dynamic variations in operating conditions and complex noise interference present limitations for existing fault diagnosis methods in processing non-stationary signals and capturing complex [...] Read more.
As a core component of the gearbox, bearings are crucial to the stability and reliability of the transmission system. However, dynamic variations in operating conditions and complex noise interference present limitations for existing fault diagnosis methods in processing non-stationary signals and capturing complex features. To address the aforementioned challenges, this paper proposes a bearing fault diagnosis method based on a multi-feature fusion dual-channel CNN-Transformer-CAM framework. The model cross-fuses the two-dimensional feature images from Gramian Angular Difference Field (GADF) and Generalized S Transform (GST), preserving complete time–frequency domain information. First, a dual-channel parallel convolutional structure is employed to separately sample the generalized S-transform (GST) maps and the Gramian Angular Difference Field (GADF) maps, enriching fault information from different dimensions and effectively enhancing the model’s feature extraction capability. Subsequently, a Transformer structure is introduced at the backend of the convolutional neural network to strengthen the representation and analysis of complex time–frequency features. Finally, a cross-attention mechanism is applied to dynamically adjust features from the two channels, achieving adaptive weighted fusion. Test results demonstrate that under conditions of noise interference, limited samples, and multiple operating states, the proposed method can effectively achieve the accurate assessment of bearing fault 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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22 pages, 1402 KB  
Article
Spacecraft Health Status Monitoring Method Based on Multidimensional Data Fusion
by Hanyu Liang, Chengrui Liu, Wenjing Liu, Wenbo Li and Yan Zhang
Machines 2025, 13(12), 1136; https://doi.org/10.3390/machines13121136 - 12 Dec 2025
Viewed by 726
Abstract
To address the difficulty of detecting on-orbit faults of spacecraft under complex operating conditions in time, rational monitoring and assessment of spacecraft health status are essential for ensuring its safe, stable, and reliable operation. Considering the complexity, coupling, and multidimensionality of telemetry data, [...] Read more.
To address the difficulty of detecting on-orbit faults of spacecraft under complex operating conditions in time, rational monitoring and assessment of spacecraft health status are essential for ensuring its safe, stable, and reliable operation. Considering the complexity, coupling, and multidimensionality of telemetry data, this paper proposes a method for monitoring the health status of spacecraft based on multidimensional data fusion for a key electromechanical component of a spacecraft control system. The method first extracts the explicit and implicit features of the multidimensional coupled telemetry parameters via physical feature formulas and a stacked autoencoder. Then, the extracted features are fused and filtered to obtain the health factor—a performance degradation trend described the evolution law of key component health status over runtime. Moreover, the different degradation stages are identified via an unsupervised clustering algorithm. Finally, a Bidirectional Long Short-Term Memory (Bi-LSTM) is used to construct a health status prediction model in stages. By taking Control Moment Gyroscopes (CMGs) as experimental verification subjects, the proposed method demonstrates significantly superior performance compared to other methods across prediction accuracy metrics including MSE, RMSE, and R2. This study provides robust technical support for health status monitoring of key spacecraft electromechanical components under specific fault modes. 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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20 pages, 2614 KB  
Article
Adaptive Remaining Useful Life Estimation of Rolling Bearings Using an Incremental Unscented Kalman Filter with Nonlinear Degradation Tracking
by Xiangdian Shang, Junxing Li, Taishan Lou, Zhihua Wang, Xiaoxu Pang and Zhiwen Zhang
Machines 2025, 13(11), 1058; https://doi.org/10.3390/machines13111058 - 16 Nov 2025
Cited by 2 | Viewed by 730
Abstract
In consideration of the characteristics of two-stage (stable and degraded), nonlinearity and non-stationary randomness in the full life-cycle evolution process of the rolling bearing health indicator (HI), a novel remaining useful life (RUL) prediction method for rolling bearings is proposed based on long [...] Read more.
In consideration of the characteristics of two-stage (stable and degraded), nonlinearity and non-stationary randomness in the full life-cycle evolution process of the rolling bearing health indicator (HI), a novel remaining useful life (RUL) prediction method for rolling bearings is proposed based on long short-term memory network–Mahalanobis distance (LSTM-MD) and an incremental unscented Kalman filter (IUKF). First, an LSTM-MD hybrid algorithm is developed to precisely identify the critical change point (CP) between stable operation and incipient degradation in bearing HI trajectories, effectively mitigating the susceptibility of conventional threshold-based methods to HI fluctuations. Second, during the degradation stage, a degradation analysis model based on the nonlinear Wiener process is constructed. Simultaneously, an IUKF-based RUL prediction method for bearings is proposed, which overcomes the implicit assumption of the traditional UKF method that one-step prediction can replace state prediction, particularly in scenarios with significant HI fluctuations, thereby significantly reducing prediction errors. Finally, the proposed method is validated through comparisons with traditional methods using both the XJTU-SY public dataset and a self-built bearing test dataset. The results demonstrate that compared to traditional methods, the accuracy of initial degradation change point identification is improved by 32.6%, and the root mean square error (MSE) of RUL prediction is decreased by 41.8%. 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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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 717
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 2 | Viewed by 1295
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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