Design of an Improved Remaining Useful Life Prediction Model Based on Vibration Signals of Wind Turbine Rotating Components

Abstract

Faults in wind turbine rotating components contribute significantly to malfunctions and downtime. A prevalent strategy to reduce the Cost of Energy (CoE) in wind energy production focuses on minimizing maintenance expenses associated with these turbine components. An accurate Remaining Useful Life (RUL) diagnosis of these components is crucial for maintenance planning, ensuring uninterrupted energy quality and cost-efficiency. This paper introduces a refined method for RUL prediction of wind turbine rotating components using a Health Index (HI) derived from vibration signals. Performing HI construction by extracting all features from the vibration signal and selecting the best features to build HIs using on Principal Component Analysis (PCA) and some abnormal areas that deviate from the bearing damage trend can be eliminated. After constructing a HI use the similarity model and degradation models to predict RUL. Research results show that this degradation method can provide a reliable means to predict the RUL of wind turbine rotating components based on vibration signals. More importantly, predicting RUL in this way can significantly reduce operating and maintenance costs by providing wind turbine rotating operators with sufficient advance notice to plan repairs or replacements before any component failure occurs.