Fault Prognosis and Remaining Useful Life Prediction of Wind Turbine Gearboxes Using Current Signal Analysis

Abstract

Gearbox faults constitute a significant portion of all faults and contribute to a significant portion of the downtime of wind turbines. Thus, an accurate prediction of the gearbox remaining useful life (RUL) is important to achieve condition-based maintenance to ensure secure and reliable operations of wind turbines and reduce the cost of wind power. This, however, is a challenging work due to the lack of accurate physical degradation models and limited data. This paper proposes a new fault prognostic and RUL prediction method for gearboxes based on the adaptive neuro-fuzzy inference system (ANFIS) and particle filtering (PF) approaches. In the proposed method, the fault feature is extracted from the measured one phase stator current of the generator connected with the gearbox; the ANFIS learns the state transition function of the extracted fault feature; the PF algorithm then predicts the RUL of the gearbox based on the learned state transition function and new information of the fault feature. Experimental results on a gearbox run-to-failure test are provided to show the effectiveness of the proposed method.