Deep domain adversarial residual neural network for sustainable wind turbine cyber‐physical system fault diagnosis-Reference-Cited by-同舟云学术

Deep domain adversarial residual neural network for sustainable wind turbine cyber‐physical system fault diagnosis

Published:2021-03-04 Issue:11 Volume:51 Page:2128-2142
ISSN:0038-0644
Container-title:Software: Practice and Experience
language:en
Short-container-title:Softw Pract Exper

Author:

Jin Yanrui¹^ORCID,Feng Qiang²,Zhang Xiping³,Lu Peili⁴,Shen Jiaqi⁵,Tu Yihui⁵,Wu Zhiquan⁶

Affiliation:

1. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

2. Datang Renewable Energy Research Institute, Co. Ltd Beijing China

3. Distributed Energy and Multi Energy Complementary Center, Renewable Energy Science and Technology Research Institute China Datang Corporation Beijing China

4. Shanghai Minghuan Technology Co., Ltd Shanghai China

5. School of Mathematical Science Shanghai Jiao Tong University Shanghai China

6. State Power Investment Corporation Research Institute, Co. Ltd Beijing China

Publisher

Wiley

Subject

Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.2937

Reference41 articles.

1. Value of wind power – Implications from specific power

2. Opportunities and challenges for a sustainable energy future

3. An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples

4. Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests

5. Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing

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