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Electronics
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Data Science in Prognostics and Health Management of Industrial Equipment
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Data Science in Prognostics and Health Management of Industrial Equipment

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 2372

Special Issue Editors

Dr. Ruyi Huang

E-Mail Website
Guest Editor
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China
Interests: intelligent fault diagnosis; prognostics and health management; multisensory data/information fusion technology; interpretable industrial AI-based methods
Special Issues, Collections and Topics in MDPI journals
Dr. Xi Zhang

E-Mail Website
Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 400065, China
Interests: digital twin; deep learning; structural health monitoring
Dr. Jianguo Miao

E-Mail Website
Guest Editor
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: deep learning; pattern recognition; intelligent fault diagnosis

Special Issue Information

Dear Colleagues,

Currently, data science plays a crucial role in the field of Prognostics and Health Management (PHM) of industrial equipment, revolutionizing the way we monitor, diagnose, predict, and maintain equipment health. By leveraging advanced data analytics techniques, data science enables us to extract valuable insights from vast amounts of data generated by equipment sensors and monitoring systems. However, in various industrial scenarios, there still exist many unsolved problems or challenges within the PHM field, for instance, zero-shot or few-shot diagnosis tasks, interpretability models, noisy signal processing, usage of simulation data, etc. Researchers are actively exploring innovative approaches to tackle these persistent issues and enhance the effectiveness of PHM practices. Many journal papers or conference papers aiming at these problems have been published. But, for different equipment objects, it is difficult to determine a general, mature, and feasible solution. That implies that the field of PHM requires a wide range of diverse research to adapt to various scenarios. Therefore, this Special Issue welcomes all research dedicated to solving problems related to the field of PHM. We believe that your research can contribute valuable insights to PHM, whether in algorithms/methods, frameworks, or applications.

The scopes of this Special Issue included but are not limited to the following:

  1. Big Data Analytics for Industrial PHM;
  2. Internet of Things (IoT) and Sensor Data Fusion in PHM;
  3. Large Language Models (LLMs) for Industrial PHM;
  4. Explainable Artificial Intelligence in PHM;
  5. Digital Twins for Industrial Equipment Maintenance;
  6. Reliability or Trustworthy PHM in Practical Industry;
  7. Uncertainty Quantification for Data-Driven PHM Methods;
  8. Federated Learning or Edge Computing for Real-Time PHM;
  9. Advanced Signal Processing Methods for Industrial PHM;
  10. Real-World Case Studies and Applications of PHM.

Dr. Ruyi Huang
Dr. Xi Zhang
Dr. Jianguo Miao
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. Electronics 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 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

  • deep learning technologies in PHM
  • fault diagnosis and prediction
  • equipment reliability
  • digital twin
  • signal processing technologies
  • interpretability
  • data security and privacy protection

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

15 pages, 3296 KiB  
Article
Leveraging Pre-Trained GPT Models for Equipment Remaining Useful Life Prognostics
by Haoliang Cui, Xiansheng Guo and Liyang Yu
Electronics 2025, 14(7), 1265; https://doi.org/10.3390/electronics14071265 - 23 Mar 2025
Viewed by 387
Abstract
Remaining Useful Life (RUL) prediction is crucial for optimizing predictive maintenance and resource management in industrial machinery. However, existing methods struggle with rigid spatiotemporal feature fusion, difficulty in capturing long-term dependencies, and poor performance on small datasets. To address these challenges, we propose [...] Read more.
Remaining Useful Life (RUL) prediction is crucial for optimizing predictive maintenance and resource management in industrial machinery. However, existing methods struggle with rigid spatiotemporal feature fusion, difficulty in capturing long-term dependencies, and poor performance on small datasets. To address these challenges, we propose a GPT-based RUL prediction model that enhances feature integration flexibility while leveraging few-shot learning and cross-modal knowledge transfer for improved accuracy in both data-rich and data-limited scenarios. Experiments on the NASA N-CMAPSS dataset show that our model outperforms state-of-the-art methods across multiple metrics, enabling more precise maintenance, cost optimization, and sustainable operations. Full article
(This article belongs to the Special Issue Data Science in Prognostics and Health Management of Industrial Equipment)
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27 pages, 58601 KiB  
Article
Speed–Load Insensitive Fault Diagnosis Method of Wind Turbine Gearbox Based on Adversarial Training
by Wenjie Zhou, Quan Zhou and Jie Zhang
Electronics 2025, 14(4), 732; https://doi.org/10.3390/electronics14040732 - 13 Feb 2025
Viewed by 593
Abstract
The rotational speed and load torque of wind turbine gearboxes can vary widely during operation, which has an obvious impact on the gearbox fault diagnosis carried out based on vibration signals. To address this problem, this paper proposes a fault diagnosis method that [...] Read more.
The rotational speed and load torque of wind turbine gearboxes can vary widely during operation, which has an obvious impact on the gearbox fault diagnosis carried out based on vibration signals. To address this problem, this paper proposes a fault diagnosis method that introduces an adversarial training mechanism and designs a game learning strategy among the feature extractor, fault recognizer, rotational speed estimator, and load estimator. In this way, the network tends to acquire fault features with weaker correlation with rotational speed and load and thus improves the performance of the fault diagnosis network in the face of the samples from the rotational speed and load ranges that are not covered by the training set. At the same time, in order to verify the effectiveness of the proposed method, in this paper, we have designed an experimental platform for wind turbine gearbox scaling, carried out simulation experiments of variable speed and torque faults, collected experimental data, and constructed a variable speed and load fault dataset. Comparing the proposed method with the baseline model, when confronted with data from RPMs or load ranges not covered by the training set, the accuracy of the baseline model drops by anywhere from 10.54% to 16.46%, while the accuracy of the method drops by only 1.39%. The results show that the method can effectively improve the performance of the fault diagnosis network when facing a variation of speed and load. Full article
(This article belongs to the Special Issue Data Science in Prognostics and Health Management of Industrial Equipment)
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19 pages, 6153 KiB  
Article
Multi-Objective Topology Optimization of Thin-Plate Structures Based on the Stiffener Size and Layout
by Qin Yin, Junsong Guo, Yingzhe Kan, Jinghua Ma and Congying Deng
Electronics 2024, 13(24), 4968; https://doi.org/10.3390/electronics13244968 - 17 Dec 2024
Viewed by 916
Abstract
To address the limitations of existing optimization methods that focus on single objectives or neglect stiffener features, a multi-objective topology optimization (MOTO) method is proposed based on the stiffener size and layout. By constraining the initial structural performance parameters, the optimal stiffener height [...] Read more.
To address the limitations of existing optimization methods that focus on single objectives or neglect stiffener features, a multi-objective topology optimization (MOTO) method is proposed based on the stiffener size and layout. By constraining the initial structural performance parameters, the optimal stiffener height is determined through size optimization. Based on the stiffener height, single-objective topology optimization is used to achieve the best material distribution. The stiffener width is treated as a design variable, while MOTO is performed on the load point displacement, first natural frequency, and mass, thereby yielding an optimal stiffener width and performance. Finally, a multi-dimensional analysis of the stiffener height, width, and dynamic and static characteristics of the stiffened thin-plate structure is conducted. The results indicate that the optimized stiffener layout is considerably improved. Compared to the initial structure, the maximum and average displacements of the load point are reduced by 23.26% and 8.62%, respectively. The first natural frequency increases by 3.81%, while the maximum resonance amplitude and overall structural mass decrease by 39.97% and 1.99%, respectively. The results indicate that the optimized structure achieves a lightweight design while maintaining better stiffness and low-frequency vibration resistance. The feasibility and effectiveness of the proposed method are validated. Full article
(This article belongs to the Special Issue Data Science in Prognostics and Health Management of Industrial Equipment)
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