RUL forecasting for wind turbine predictive maintenance based on deep learning

TL;DR

This paper presents a deep learning approach using attention mechanisms to accurately forecast the remaining useful life of wind turbines, enabling more effective predictive maintenance in remote wind farms.

Contribution

It introduces two novel deep learning models, ForeNet-2d and ForeNet-3d, that predict wind turbine RUL with minimal human feature engineering and high accuracy.

Findings

Forecasts deviate by only 10 minutes to 1.8 days from actual RUL.

Models successfully predict RUL with a 2-week window.

Most predictions are within a few hours of true RUL.

Abstract

Predictive maintenance (PdM) is increasingly pursued to reduce wind farm operation and maintenance costs by accurately predicting the remaining useful life (RUL) and strategically scheduling maintenance. However, the remoteness of wind farms often renders current methodologies ineffective, as they fail to provide a sufficiently reliable advance time window for maintenance planning, limiting PdM's practicality. This study introduces a novel deep learning (DL) methodology for future RUL forecasting. By employing a multi-parametric attention-based DL approach that bypasses feature engineering, thereby minimizing the risk of human error, two models: ForeNet-2d and ForeNet-3d are proposed. These models successfully forecast the RUL for seven multifaceted wind turbine (WT) failures with a 2-week forecast window. The most precise forecast deviated by only 10 minutes from the actual RUL, while the least accurate prediction deviated by 1.8 days, with most predictions being off by only a few hours. This methodology offers a substantial time frame to access remote WTs and perform necessary maintenance, thereby enabling the practical implementation of PdM.