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Volume 13
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10.3390/app13084776
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Article

Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin

by
Yi Wang
,
Wenlei Sun
*,
Liqiang Liu
,
Bingkai Wang
,
Shenghui Bao
and
Renben Jiang
School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830046, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(8), 4776; https://doi.org/10.3390/app13084776
Submission received: 15 March 2023 / Revised: 1 April 2023 / Accepted: 6 April 2023 / Published: 10 April 2023
(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications)
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Abstract

Aiming at the problems of the traditional planetary gear fault diagnosis method of wind turbines, such as the poor timeliness of data transmission, weak visualization effect of state monitoring, and untimely feedback of fault information, this paper proposes a planetary gear fault diagnosis method for wind turbines based on a digital twin. The method was used to build the digital twin model of wind turbines and analyze the wind turbines’ operating state utilizing virtual and real data. Empirical mode decomposition (EMD) was used, and an atom search optimization–support vector machine (ASO-SVM) model was established for planetary gear fault diagnosis. The digital twin model diagnoses faults and constantly revises the model based on the diagnostic results. The digital twin fault diagnosis system was implemented in the Unity3D platform. The experimental results demonstrate the feasibility of the proposed early-warning system for the real-time diagnosis of planetary gear faults in wind turbines.
Keywords: wind turbine; digital twin; fault diagnosis; real-time perception; Unity3D wind turbine; digital twin; fault diagnosis; real-time perception; Unity3D

Share and Cite

MDPI and ACS Style

Wang, Y.; Sun, W.; Liu, L.; Wang, B.; Bao, S.; Jiang, R. Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin. Appl. Sci. 2023, 13, 4776. https://doi.org/10.3390/app13084776

AMA Style

Wang Y, Sun W, Liu L, Wang B, Bao S, Jiang R. Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin. Applied Sciences. 2023; 13(8):4776. https://doi.org/10.3390/app13084776

Chicago/Turabian Style

Wang, Yi, Wenlei Sun, Liqiang Liu, Bingkai Wang, Shenghui Bao, and Renben Jiang. 2023. "Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin" Applied Sciences 13, no. 8: 4776. https://doi.org/10.3390/app13084776

APA Style

Wang, Y., Sun, W., Liu, L., Wang, B., Bao, S., & Jiang, R. (2023). Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin. Applied Sciences, 13(8), 4776. https://doi.org/10.3390/app13084776

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