Open Access

ARTICLE

A Fusion Optimization Method for Remaining Useful Life Prediction of Wind Turbine Gearboxes

Wei Chen, Zhi Wei^*, Tingting Pei, Jianghao Zhu, Yang Wu

School of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

* Corresponding Author: Zhi Wei. Email:

Energy Engineering 2026, 123(6), 22 https://doi.org/10.32604/ee.2025.073843

Received 26 September 2025; Accepted 25 November 2025; Issue published 27 May 2026

Abstract

Wind turbine gearboxes are critical components in large-scale power generation systems, and their unexpected failures often result in significant economic losses, long downtime, and decreased energy efficiency. Accurate prediction of their Remaining Useful Life (RUL) is therefore vital for enhancing operational reliability, implementing condition-based maintenance, and optimizing lifecycle management. However, existing approaches often neglect the memory effect in degradation processes and fail to establish an effective interaction between stochastic degradation modeling and RUL prediction. To address these challenges, this study proposes a novel fusion method that integrates a stochastic degradation model with an intelligent prediction framework. The degradation model employs Fractional Brownian Motion (FBM) to capture long-range dependence and memory effects in gearbox performance, while the prediction framework leverages an enhanced recurrent neural network optimized through evolutionary mechanisms. By linking degradation modeling with RUL prediction through parameter optimization, the proposed method strengthens the interaction between physical degradation and data-driven prediction. Simulation results based on gearbox datasets demonstrate that the proposed approach significantly improves RUL prediction performance, achieving a 23.2% reduction in RMSE¯, a 26.7% improvement in SF¯, and a 3.3% increase in R2 compared with traditional RNN and LSTM models, highlighting its potential for practical deployment in wind farm operations to support proactive maintenance scheduling and enhance system reliability.

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Keywords

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