LAPSE:2023.26108
Published Article
LAPSE:2023.26108
Condition Maintenance Decision of Wind Turbine Gearbox Based on Stochastic Differential Equation
Hongsheng Su, Dantong Wang, Xuping Duan
March 31, 2023
Maintenance decision analysis is necessary to ensure the safe and stable operation of wind turbine equipment. To address gearboxes with a high failure rate in wind turbines, this paper establishes a new stochastic differential equation model of gearbox state transition to maximize the utilization of gearboxes. This model divides the state of the gearbox into two parts: internal degradation and external random interference. Weibull distribution and polynomial approximation were used to construct the internal degradation model of the gearbox. The external random interference is simulated by Brownian motion. On the basis of the analysis of monitoring data, the parameters of the gearbox state model were solved using the Newton−Raphson iterative method and entropy method. The state change of the gearbox was simulated in MATLAB, and the residual value between the predicted state and the real state was calculated. Compared with the state transformation model constructed by the traditional ordinary differential equation and the gamma distribution, the Weibull polynomial approximation stochastic model can better reflect the state of the device. With reliability set as the decision goal, the maintenance time of the gearbox is predicted, and the validity of the model is verified through case analysis.
Keywords
entropy method, polynomial approximation, reliability, stochastic differential equation, Weibull distribution
Suggested Citation
Su H, Wang D, Duan X. Condition Maintenance Decision of Wind Turbine Gearbox Based on Stochastic Differential Equation. (2023). LAPSE:2023.26108
Author Affiliations
Su H: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; Rail Transit Electrical Automation Engineering Laboratory of Gansu Province, Lanzhou Jiaotong University, Lanzhou 730070, China
Wang D: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; Rail Transit Electrical Automation Engineering Laboratory of Gansu Province, Lanzhou Jiaotong University, Lanzhou 730070, China
Duan X: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Journal Name
Energies
Volume
13
Issue
17
Article Number
E4480
Year
2020
Publication Date
2020-08-31
Published Version
ISSN
1996-1073
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PII: en13174480, Publication Type: Journal Article
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LAPSE:2023.26108
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doi:10.3390/en13174480
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