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Machines
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Machine Learning for Fault Diagnosis of Wind Turbines
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Machine Learning for Fault Diagnosis of Wind Turbines

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 7780

Special Issue Editor

Dr. Gang Yu

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology at Shenzhen, Shenzhen 518052, China
Interests: process monitoring; fault diagnosis and prediction mechanical system signal processing intelligent maintenance system target tracking; action recognition and unknown environment navigation of service robot
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing consumption of fossil fuels and problems with the gradual deterioration in the environment, there is an urgent need to find a clean and renewable energy source. Wind energy is irreplaceable in energy structures owing to its rapid growth. Usually, wind power generators are installed in remote areas or offshore areas where traffic is inconvenient, and the gearbox is generally installed in the sky, tens or even hundreds of meters above the ground, and subjected to complex operating conditions, which makes daily monitoring and maintenance of wind turbines difficult. Once a problem occurs, it significantly reduces profits for a wind farm. Therefore, fault diagnosis and maintenance are very important during the operation of wind turbines.

In recent years, machine learning has played a crucial role as an emerging technology for fault diagnosis in wind power systems. Over recent decades, researchers have proposed different methodologies for dealing with the issues related to the fault diagnosis of wind turbines; there are still some challenges encountered in many aspects. Advances in machine learning can provide the tools and foundations for creating fascinating data-driven end-to-end solutions for the fault diagnosis of wind turbines.

This Special Issue invites researchers and industrial professionals to investigate and present recent advances and techniques addressing problems in the fault diagnosis of wind turbine using machine learning.

Dr. Gang Yu
Guest Editor

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

  • machine learning
  • fault diagnosis
  • wind turbine
  • deep learning
  • condition monitoring

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Related Special Issue

Published Papers (3 papers)

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Research

15 pages, 4404 KiB  
Article
Exploratory Analysis of SCADA Data from Wind Turbines Using the K-Means Clustering Algorithm for Predictive Maintenance Purposes
by Pablo Cosa Rodriguez, Pere Marti-Puig, Cesar F. Caiafa, Moisès Serra-Serra, Jordi Cusidó and Jordi Solé-Casals
Machines 2023, 11(2), 270; https://doi.org/10.3390/machines11020270 - 10 Feb 2023
Cited by 11 | Viewed by 2342
Abstract
Product maintenance costs throughout the product’s lifetime can account for between 30–60% of total operating costs, making it necessary to implement maintenance strategies. This problem not only affects the economy but is also related to the impact on the environment, since breakdowns are [...] Read more.
Product maintenance costs throughout the product’s lifetime can account for between 30–60% of total operating costs, making it necessary to implement maintenance strategies. This problem not only affects the economy but is also related to the impact on the environment, since breakdowns are also responsible for the delivery of greenhouse gases. Industrial maintenance is a set of measures of a technical-organizational nature whose purpose is to sustain the functionality of the equipment and guarantee an optimal state of the machines over time, with the aim of saving costs, extending the useful life of the machines, saving energy, maximising production and availability, ensuring the quality of the product obtained, providing job security for technicians, preserving the environment, and reducing emissions as much as possible. Machine learning techniques can be used to detect or predict faults in wind turbines. However, labelled data suffers from many problems in this application because alarms are usually not clearly associated with a specific fault, some labels are wrongly associated with a problem, and the imbalance between labels is evident. To avoid using labelled data, we investigate here the use of the clustering technique, more specifically K-means, and boxplot representations of the variables for a set of six different tests. Experimental results show that in some cases, the clustering and boxplot techniques allow us to determine outliers or identify erroneous behaviours of the wind turbines. These cases can then be investigated in detail by a specialist so that more efficient predictive maintenance can be carried out. Full article
(This article belongs to the Special Issue Machine Learning for Fault Diagnosis of Wind Turbines)
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15 pages, 11343 KiB  
Article
A Lightweight CNN for Wind Turbine Blade Defect Detection Based on Spectrograms
by Yuefan Zhu and Xiaoying Liu
Machines 2023, 11(1), 99; https://doi.org/10.3390/machines11010099 - 11 Jan 2023
Cited by 8 | Viewed by 2392
Abstract
Since wind turbines are exposed to harsh working environments and variable weather conditions, wind turbine blade condition monitoring is critical to prevent unscheduled downtime and loss. Realizing that common convolutional neural networks are difficult to use in embedded devices, a lightweight convolutional neural [...] Read more.
Since wind turbines are exposed to harsh working environments and variable weather conditions, wind turbine blade condition monitoring is critical to prevent unscheduled downtime and loss. Realizing that common convolutional neural networks are difficult to use in embedded devices, a lightweight convolutional neural network for wind turbine blades (WTBMobileNet) based on spectrograms is proposed, reducing computation and size with a high accuracy. Compared to baseline models, WTBMobileNet without data augmentation has an accuracy of 97.05%, a parameter of 0.315 million, and a computation of 0.423 giga floating point operations (GFLOPs), which is 9.4 times smaller and 2.7 times less computation than the best-performing model with only a 1.68% decrease in accuracy. Then, the impact of difference data augmentation is analyzed. The WTBMobileNet with augmentation has an accuracy of 98.1%, and the accuracy of each category is above 95%. Furthermore, the interpretability and transparency of WTBMobileNet are demonstrated through class activation mapping for reliable deployment. Finally, WTBMobileNet is explored in drones image classification and spectrogram object detection, whose accuracy and mAP@[0.5, 0.95] are 89.55% and 70.7%, respectively. This proves that WTBMobileNet not only has a good performance in spectrogram classification, but also has good application potential in drone image classification and spectrogram object detection. Full article
(This article belongs to the Special Issue Machine Learning for Fault Diagnosis of Wind Turbines)
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21 pages, 5085 KiB  
Article
Federated Multi-Model Transfer Learning-Based Fault Diagnosis with Peer-to-Peer Network for Wind Turbine Cluster
by Wanqian Yang and Gang Yu
Machines 2022, 10(11), 972; https://doi.org/10.3390/machines10110972 - 24 Oct 2022
Cited by 6 | Viewed by 2138
Abstract
Intelligent fault diagnosis for a single wind turbine is hindered by the lack of sufficient useful data, while multi-turbines have various faults, resulting in complex distributions. Collaborative intelligence can better solve these problems. Therefore, a peer-to-peer network is constructed with one node corresponding [...] Read more.
Intelligent fault diagnosis for a single wind turbine is hindered by the lack of sufficient useful data, while multi-turbines have various faults, resulting in complex distributions. Collaborative intelligence can better solve these problems. Therefore, a peer-to-peer network is constructed with one node corresponding to one wind turbine in a cluster. Each node is equivalent and functional replicable with a new federated transfer learning method, including model transfer based on multi-task learning and model fusion based on dynamic adaptive weight adjustment. Models with convolutional neural networks are trained locally and transmitted among the nodes. A solution for the processes of data management, information transmission, model transfer and fusion is provided. Experiments are conducted on a fault signal testing bed and bearing dataset of Case Western Reserve University. The results show the excellent performance of the method for fault diagnosis of a gearbox in a wind turbine cluster. Full article
(This article belongs to the Special Issue Machine Learning for Fault Diagnosis of Wind Turbines)
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