Shah Syed Shazaib, Daoliang Tan, Kumar Sah Chandan
School of Energy and Power, Beihang University, Beijing, 100191, PR China.
School of Software, Beihang University, Beijing, 100191, PR China.
Heliyon. 2024 Oct 15;10(20):e39268. doi: 10.1016/j.heliyon.2024.e39268. eCollection 2024 Oct 30.
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.
为了通过准确预测剩余使用寿命（RUL）并合理安排维护计划来降低风电场的运营和维护成本，预测性维护（PdM）的应用越来越广泛。然而，风电场位置偏远，常常导致当前方法失效，因为这些方法无法为维护计划提供足够可靠的提前时间窗口，限制了PdM的实用性。本研究引入了一种用于预测未来RUL的新型深度学习（DL）方法。通过采用基于多参数注意力的DL方法，绕过特征工程，从而将人为错误风险降至最低，提出了两种模型——ForeNet-2d和ForeNet-3d。这些模型成功地在2周的预测窗口内预测了七种多方面风力涡轮机（WT）故障的RUL。最精确的预测与实际RUL的偏差仅为10分钟，而最不准确的预测偏差为1.8天，大多数预测偏差仅为几小时。这种方法提供了一个足够长的时间框架来检修偏远的风力涡轮机并进行必要的维护，从而实现PdM的实际应用。
