Research on Wind Turbine Fault Diagnosis Technology Based on Big Data

Author:

Yang Li1ORCID,Gao Wenchao1ORCID,Liu Yi1ORCID,Zheng Mocun1ORCID,Zhang Jie1ORCID,Yang Hengyu1ORCID

Affiliation:

1. China University of Mining and Technology-Beijing, School of Artificial Intelligence, China

Funder

MOE Collaborative Projects between Industry and Education for Jointly Cultivating Talents

Beijing Association of Higher Education Project

National Research Project on Higher Education in the Coal Industry

Publisher

ACM

Reference15 articles.

1. Chen, ChangSheng. Research on Daily Maintenance and Fault Handling of Wind Turbine Units. Papermaking Equipment and Materials[J], 2021,50(12), 34-36

2. Bagde P Vanalkar A V Ikhar S R .INNOVATIVE METHODS OF MODELING GEAR FAULTS[J].[2023-09-01]

3. Trifonov M Prochazka K F Saleh Krüger.Robust Control of an Input-redundant Aircraft against Atmospheric Disturbances and Actuator Faults[J]. 2019.DOI:10.18178/ijmerr.8.6.905-910

4. Lee J Y Lee W T Ko S H et al.Fault Classification and Diagnosis of UAV motor Based on Estimated Nonlinear Parameter of Steady-State Model[J]. 2020.DOI:10.18178/ijmerr.10.1.22-31

5. Zhang Xin, Xu Zunyi, He Huiru, Wang Fei. Wind turbine blade cracking fault prediction based on RBM and SVM [J].Power system protection and control, 2020, 13 (15) : 134-140. The DOI: 10.19783 / j.carol carroll nki PSPC.191093

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