An Adaptive Generalized Cauchy Model for Remaining Useful Life Prediction of Wind Turbine Gearboxes with Long-Range Dependence

Abstract

Remaining useful life (RUL) prediction is important for wind turbine operation and maintenance. The degradation process of gearboxes in wind turbines is a slowly and randomly changing process with long-range dependence (LRD). The degradation trend of the gearbox is constantly changing, and a single drift coefficient is not accurate enough to describe the degradation trend. This paper proposes an original adaptive generalized Cauchy (GC) model with LRD and randomness to predict the RUL of wind turbine gearboxes. The LRD is explained jointly by the fractal dimension and the Hurst exponent, and the randomness is explained by the diffusion term driven by the GC difference time sequence. The estimated value of the unknown parameter of adaptive GC model is deduced, and the specific expression of the RUL estimation is deduced. The adaptability is manifested in the time-varying drift coefficient of the GC model: by continuously updating the drift coefficient to adapt to the change in the degradation trend, the adaptive GC model offers high accuracy in the prediction of the degradation trend. The performance of the proposed model is analyzed using real wind turbine gearbox data.