A novel multi-task transfer model to realize unsupervised fault diagnosis of newly constructed wind turbines under variable conditions

Xiaobo Liu; Desheng Sha; Qing Zhang; Qian Li; Xin Zou

doi:10.1117/12.3026262

1 April 2024 A novel multi-task transfer model to realize unsupervised fault diagnosis of newly constructed wind turbines under variable conditions

Xiaobo Liu, Desheng Sha, Qing Zhang, Qian Li, Xin Zou

Author Affiliations +

Proceedings Volume 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023); 1308210 (2024) https://doi.org/10.1117/12.3026262
Event: 2023 4th International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology (MEMAT 2023), 2023, Guilin, China

Abstract

Fault diagnosis can effectively improve the power generation of the wind turbines. Deep learning has promoted the intelligent development of wind turbine fault diagnosis. Traditional deep learning usually requires a sufficient amount of labeled data. However, for newly constructed wind turbines, there are problems such as insufficient samples, limited labels, variable operating conditions. Transfer learning provides a new way to solve these problems. Establishing appropriate models to reduce the distribution differences between existing and newly built units is the key to improving unsupervised fault diagnosis accuracy for newly built units. To address these challenges, a novel multi-task transfer model based on improved model agnostic meta learning (MT-TL-MAML) was proposed to realize unsupervised fault diagnosis of newly constructed wind turbines. The proposed model utilizes the advantages of MAML in generalization of new tasks and small sample diagnosis, and can adapt to randomly changing working conditions. By means of iterative learning, the gap between source domain and target domain is further narrowed, and the classifier can realize more accurate diagnosis of target domain data. This article takes the SCADA and CMS data of two wind farms as case studies to conduct unsupervised fault diagnosis and compare it with other literatures. The results validate the advantages of the proposed model in unsupervised fault diagnosis of newly constructed wind turbines.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaobo Liu, Desheng Sha, Qing Zhang, Qian Li, and Xin Zou "A novel multi-task transfer model to realize unsupervised fault diagnosis of newly constructed wind turbines under variable conditions", Proc. SPIE 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023), 1308210 (1 April 2024); https://doi.org/10.1117/12.3026262

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Wind turbine technology

Education and training

Curium

Neural networks

Statistical modeling

Detection and tracking algorithms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years