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Machines
Special Issues
Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines
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Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 8307

Editors

Prof. Dr. Ronghua Zhu

E-Mail Website
Guest Editor
Ocean College, Zhejiang University, Zhoushan 316021, China
Interests: offshore wind power; health monitoring and fault diagnosis; offshore platform structures; marine engineering structure design; high-end marine engineering equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Cailiang Zhang

E-Mail Website
Guest Editor
School of Software & Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, China
Interests: offshore wind power; signal processing; health monitoring and fault diagnosis; energy harvesting and wireless sensing
Dr. Chaoge Wang

E-Mail Website
Guest Editor
College of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: health monitoring; intelligent operation; maintenance; signal processing; intelligent fault diagnosis; remaining useful life prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Zepeng Liu

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: digital signal processing; tool condition monitoring; fault diagnosis; power systems analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous global growth in demand for clean energy, wind power generation, as a significant form of renewable energy generation, has been widely adopted. Wind turbine generators are typically installed in either terrestrial or marine environments. During long-term operation, due to the complex and harsh environments they are exposed to, various components of the units are prone to wear, fatigue, and other faults. In particular, the drive train system, as a critical link in energy transfer within wind turbine generators, directly impacts the units' power generation efficiency, operational reliability, and service life. Therefore, conducting research on the health condition monitoring and intelligent operation and maintenance (O&M) of wind turbine generators is of great significance for ensuring the stable operation of wind power generation systems, reducing O&M costs, and improving energy utilization efficiency.

This Special Issue focuses on the health condition monitoring and intelligent O&M of wind turbine generators. It aims to gather the latest research findings and advancements in relevant fields both domestically and internationally, facilitate academic exchanges, and promote the development and application of intelligent O&M technologies for wind turbines. The topics of interest for this Special Issue include, but are not limited to, the following:

  • Intelligent sensing technologies;
  • Advanced signal processing algorithms;
  • Dynamic modeling and fault simulation of key components;
  • Fault warning and identification of key components in wind turbine generators;
  • Remaining useful life prediction based on deep learning;
  • Digital twin-driven fault diagnosis of wind turbines;
  • Knowledge graph and large-model technologies;
  • Research on O&M technologies and modes for offshore wind power in deep and far sea areas;
  • Predictive maintenance strategies for wind turbine generators.

Prof. Dr. Ronghua Zhu
Dr. Cailiang Zhang
Dr. Chaoge Wang
Dr. Zepeng 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

  • wind turbines
  • digital twin
  • fault diagnosis
  • predictive maintenance

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Published Papers (6 papers)

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Research

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27 pages, 39069 KB  
Article
CESIgram: A Fault Feature Extraction Method for Rolling Bearings in Wind Turbine Equipment Based on Collaborative Filtering Correlation Spectrum
by Junjie Zhu, Yang Ding, Hui Li, Bo Wang, Dongbing Su and Yonggang Xu
Machines 2026, 14(8), 933; https://doi.org/10.3390/machines14080933 - 13 Aug 2026
Viewed by 217
Abstract
To address the difficulty of extracting weak fault features of rolling bearings in wind turbines under strong background noise, a fault feature extraction method based on the collaborative filtering correlation spectrum, named CESIgram, is proposed. The collaborative filtering correlation spectrum (CFCS) based on [...] Read more.
To address the difficulty of extracting weak fault features of rolling bearings in wind turbines under strong background noise, a fault feature extraction method based on the collaborative filtering correlation spectrum, named CESIgram, is proposed. The collaborative filtering correlation spectrum (CFCS) based on Block Matching 3D is designed to suppress random noise while preserving cyclostationary structures, resulting in a clearer cyclic spectral representation. A projection method along the cyclic frequency axis is proposed to obtain the carrier-based enhanced envelope spectrum. An integrated envelope spectrum index combining harmonic significance and periodic impact is proposed to quantify fault feature enrichment in different enhanced envelope spectra. The method works in three stages: spectral representation via Fast-SC, reformulation of the spectral correlation via CFCS, and adaptive band selection via CESI. The method successfully extracted fault characteristic frequencies and their harmonics in simulation and experimental signals under various strong noise conditions, while Fast Kurtogram, Autogram, Infogram, and Fast Entrogram failed to detect any fault-related peaks. Comparative analysis shows that the proposed method has significant advantages in noise suppression and fault feature extraction. The effectiveness is verified using simulation and experimental signals of rolling bearing faults in wind power equipment. Full article
(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)
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22 pages, 13854 KB  
Article
Fault Diagnosis of Gearbox Bearings Under Extreme Class Imbalance Based on Multi-Resolution Windows and Density-Aware Cross-Modal Fusion
by Hao Wei, Minghui Liang, Gang Lan and Xueyi Li
Machines 2026, 14(8), 834; https://doi.org/10.3390/machines14080834 - 23 Jul 2026
Viewed by 409
Abstract
Robust bearing fault diagnosis under variable speeds and extreme class imbalance remains challenging due to the difficulty in decoupling transient-periodic features. To address this, we propose a Dual-Branch Weighted Convolution Network with Cross-Attention Fusion (DBWC). Specifically, a dual-branch architecture with spatial density-weighted kernels [...] Read more.
Robust bearing fault diagnosis under variable speeds and extreme class imbalance remains challenging due to the difficulty in decoupling transient-periodic features. To address this, we propose a Dual-Branch Weighted Convolution Network with Cross-Attention Fusion (DBWC). Specifically, a dual-branch architecture with spatial density-weighted kernels is proposed to decouple high-frequency transients from low-frequency periodic trends. Subsequently, a Cross-Attention Fusion module synthesizes these heterogeneous features by using global contexts to filter local noise. Additionally, an imbalance-aware strategy integrating Focal Loss and composite augmentation is developed to mitigate model bias. Extensive experiments on MCC5-THU and HUST benchmarks demonstrate that DBWC achieves an accuracy of 91.00% and 90.12%, respectively. The proposed method outperforms state-of-the-art models by an average margin of 5%, providing a data-efficient paradigm for complex industrial monitoring. Full article
(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)
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36 pages, 10549 KB  
Article
A Multi-Class Predictive Maintenance Framework for Jet Engines Using the C-MAPSS Dataset
by Bowen Dong, Xinyu Zhang, Lingmin Hou, Chaoya Yan, Yifan Feng, Weiyan Zhu and Lixing Lin
Machines 2026, 14(6), 695; https://doi.org/10.3390/machines14060695 - 17 Jun 2026
Viewed by 585
Abstract
Aero-engine predictive maintenance is challenged by heterogeneous operating conditions, complex degradation patterns, and the need for interpretable maintenance alerts rather than solely numerical life estimates. This study investigates a condition-aware data-driven framework for jet engine health assessment using the NASA C-MAPSS dataset, which [...] Read more.
Aero-engine predictive maintenance is challenged by heterogeneous operating conditions, complex degradation patterns, and the need for interpretable maintenance alerts rather than solely numerical life estimates. This study investigates a condition-aware data-driven framework for jet engine health assessment using the NASA C-MAPSS dataset, which contains four benchmark subsets (FD001–FD004) with different operating conditions and fault modes. Instead of formulating the task as conventional remaining useful life regression, this study reformulates degradation assessment as a three-class health state classification problem, including Normal, Warning, and Fault. A unified preprocessing pipeline is developed, incorporating condition-wise normalization, first-order differential feature construction, and per-unit sliding window segmentation to reduce operating-condition bias, capture degradation dynamics, and prevent data leakage. Five representative models are evaluated under the same framework, including XGBoost, LightGBM, Random Forest, a context-aware multi-scale temporal attention convolutional neural network, and a bidirectional long short-term memory network. The results show that the proposed framework achieves consistently high classification accuracy across all four subsets, with the best results of 0.9841 on FD001, 0.9764 on FD002, 0.9891 on FD003, and 0.9832 on FD004. In addition, Bi-LSTM outperforms MSTA-CNN on all subsets, for example improving accuracy from 0.9614 to 0.9747 on FD002 and from 0.9773 to 0.9806 on FD004, which is consistent with the importance of long-term temporal dependency modeling for this task. These findings suggest that the proposed framework provides an effective and maintenance-decision-aligned solution for C-MAPSS-based health monitoring, where the three-class alert output offers clearer operational meaning than a single numerical life estimate. Full article
(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)
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29 pages, 6187 KB  
Article
Relation Knowledge-Guided Federated Model Compression for Rare-Fault Preservation in Motor Fault Diagnosis
by Genbao Zhao and Juan Zhang
Machines 2026, 14(6), 689; https://doi.org/10.3390/machines14060689 - 15 Jun 2026
Viewed by 422
Abstract
To address global knowledge bias, weak rare-fault recognition, and high edge-deployment costs caused by heterogeneous sample sizes, data quality, fault categories, and monitoring modalities among multiple clients, this paper proposes a rare-fault-preserving federated dynamic model slimming method based on relational knowledge. The core [...] Read more.
To address global knowledge bias, weak rare-fault recognition, and high edge-deployment costs caused by heterogeneous sample sizes, data quality, fault categories, and monitoring modalities among multiple clients, this paper proposes a rare-fault-preserving federated dynamic model slimming method based on relational knowledge. The core idea is to formulate lightweight federated diagnosis as a joint optimization problem of rare-fault knowledge preservation and redundant knowledge suppression. At each local client, output-discriminative knowledge, class-prototype relations, and input-sensitive relations are extracted to describe diagnostic knowledge from the decision, structure, and weak-response levels. At the federated server, a rare-fault-aware weighting mechanism adjusts the contribution of local knowledge according to sample scarcity, output reliability, and distribution dispersion and then fuses multi-granularity relational knowledge to optimize the global teacher model. A relation-constrained gated slimming strategy is further designed for the student model, enabling the lightweight model to retain critical diagnostic channels while suppressing repetitive and low-contribution information. Experiments on the CWRU bearing dataset and the HUST multimodal motor dataset show that the proposed method achieves higher diagnostic accuracy, rare-fault recall, and deployment efficiency under composite imbalance, cross-condition generalization, and modality-missing deployment scenarios. These results demonstrate the effectiveness of the proposed method for raw-data-free and privacy-aware multi-client motor fault diagnosis. Full article
(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)
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31 pages, 7238 KB  
Article
Multimodal Fault Diagnosis of Rolling Bearings Based on GRU–ResNet–CBAM
by Kunbo Xu, Jingyang Zhang, Dongjun Liu, Chaoge Wang, Ran Wang and Funa Zhou
Machines 2026, 14(3), 318; https://doi.org/10.3390/machines14030318 - 11 Mar 2026
Cited by 1 | Viewed by 900
Abstract
Rolling bearings exhibit nonlinear and non-stationary fault signals under complex working conditions, rendering single-modal representation insufficient for accurate diagnosis. To address this limitation, this paper proposes a novel parallel multimodal fusion fault diagnosis model based on a Gated Recurrent Unit (GRU), a Residual [...] Read more.
Rolling bearings exhibit nonlinear and non-stationary fault signals under complex working conditions, rendering single-modal representation insufficient for accurate diagnosis. To address this limitation, this paper proposes a novel parallel multimodal fusion fault diagnosis model based on a Gated Recurrent Unit (GRU), a Residual Network (ResNet), and a Convolutional Block Attention Module (CBAM). First, a systematic multimodal representation selection framework is introduced, identifying the Markov Transition Field (MTF) as the optimal two-dimensional (2D) image modality due to its superior texture clarity and noise resistance compared to other methods. Second, parallel dual-branch architecture is designed to simultaneously process heterogeneous data. The 1D-GRU branch captures long-range temporal dependencies directly from raw vibration signals, while the 2D ResNet-CBAM branch extracts deep spatial features from the MTF images, adaptively focusing on key fault regions. These heterogeneous features are then fused through concatenation to retain complementary diagnostic information. Experimental validation on the Case Western Reserve University (CWRU) dataset demonstrates that the proposed model achieves a 99.57% accuracy in a 10-classification task. Furthermore, it exhibits significant parameter efficiency and outstanding robustness, with the accuracy decreasing by no more than 1.2% under noise interference and cross-load scenarios, comprehensively outperforming existing single-modal and advanced fusion methods. Full article
(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)
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Review

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19 pages, 619 KB  
Review
Condition-Based Maintenance in Complex Degradation Systems: A Review of Modeling Evolution, Multi-Component Systems, and Maintenance Strategies
by Hui Cao, Jie Yu and Fuhai Duan
Machines 2025, 13(8), 714; https://doi.org/10.3390/machines13080714 - 12 Aug 2025
Cited by 13 | Viewed by 4476
Abstract
This review systematically examines the evolution of maintenance strategies for complex systems, with a focus on the advancements in condition-based maintenance (CBM) decision-making methodologies. Traditional approaches, such as post-failure maintenance and time-based maintenance, are increasingly supplanted by CBM due to challenges like high [...] Read more.
This review systematically examines the evolution of maintenance strategies for complex systems, with a focus on the advancements in condition-based maintenance (CBM) decision-making methodologies. Traditional approaches, such as post-failure maintenance and time-based maintenance, are increasingly supplanted by CBM due to challenges like high costs or inefficiency in resource allocation. CBM leverages system reliability models in conjunction with component degradation data to dynamically establish maintenance thresholds, optimizing resource utilization while minimizing operational risks and repair costs. Research has expanded from single-component degradation systems to multi-component systems, leveraging degradation models and optimization algorithms to propose strategies addressing multi-level control limits, economic dependencies, and task constraints. Recent studies emphasize multi-component interactions, incorporating structural influences, imperfect repairs, and economic correlations into maintenance planning. Despite progress, challenges persist in modeling coupled degradation mechanisms and coordinating maintenance decisions for interdependent components. Future research directions should encompass adaptive learning strategies for dynamic degradation processes, such as those employed in intelligent agents for real-time environmental adaptation, and the incorporation of intelligent predictive technologies to enhance system performance and resource utilization. Full article
(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)
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