Abstract
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.
Highlights
In the context of the global energy crisis, wind energy has been attracting increasing attention as a clean and pollution-free renewable energy source, and the installed capacity of wind power has increased significantly
With the increase in the stock of operational equipment, the wind power operation and maintenance market has ushered in development opportunities and space, thereby becoming an important factor that affects the development of the wind power industry
In (1), the first term on the right side of the equation is the decline of the gearbox itself and λ(x(t), t) is the failure rate of the gearbox, the second term represents the random disturbance received by the gearbox, μ(x(t), t) is the random disturbance coefficient, which is called the equipment state fluctuation rate, and B(t) is the Brownian motion, which satisfies the Hypotheses 2 and 3
Summary
In the context of the global energy crisis, wind energy has been attracting increasing attention as a clean and pollution-free renewable energy source, and the installed capacity of wind power has increased significantly. When constructing the state transition model of the equipment, most of the studies did not consider the impact and interference of random factors such as daily inspection, maintenance, and repair on the system process behavior, which cannot accurately estimate the state of the equipment. Compared with the traditional ordinary differential equation, this model considers that the equipment failure rate is affected by the equipment state in addition to the time. It takes into account the influence of the external random disturbance on the equipment state and integrates the relationship between the equipment failure rate and external random interference This model can more accurately predict the state of the equipment, thereby more accurately predicting the inspection moment.
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