用深度学习精准预测风机剩余寿命，提前两周安排维护。
RUL forecasting for wind turbine predictive maintenance based on deep learning
- 用注意力机制的深度学习模型自动提取故障特征，无需人工设计特征。
- 对7类故障的寿命预测误差最小仅10分钟，多数在数小时内。
- 适合风电运维团队用于远程风机的智能预防性维护决策。
预测性维护（PdM）正被广泛追求，以通过准确预测风力发电机（WT）的剩余有用寿命（RUL）并合理安排维护来降低风电场运维成本。然而，由于风电场位置偏远，现有方法常无法提供足够可靠的提前时间窗口，限制了PdM的实际应用。本研究提出一种新型深度学习（DL）方法，采用多参数注意力机制的端到端框架，避免了特征工程，降低了人为错误风险。提出了两个模型：ForeNet-2d和ForeNet-3d，成功实现对七类复杂风机故障的未来RUL预测，预测窗口达2周。最精确预测与实际值偏差仅10分钟，最不准确预测偏差为1.8天，大多数预测误差在数小时内。该方法为远程访问风机并执行必要维护提供了充足时间，推动了PdM的实际落地。
原文摘要 · Abstract (English)
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.
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