In this study, we leverage SCADA data from diverse wind turbines to predict
power output, employing advanced time series methods, specifically Functional
Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key
innovation lies in the ensemble of FNN and LSTM models, capitalizing on their
collective learning. This ensemble approach outperforms individual models,
ensuring stable and accurate power output predictions. Additionally, machine
learning techniques are applied to detect wind turbine performance
deterioration, enabling proactive maintenance strategies and health assessment.
Crucially, our analysis reveals the uniqueness of each wind turbine,
necessitating tailored models for optimal predictions. These insight
underscores the importance of providing automatized customization for different
turbines to keep human modeling effort low. Importantly, the methodologies
developed in this analysis are not limited to wind turbines; they can be
extended to predict and optimize performance in various machinery, highlighting
the versatility and applicability of our research across diverse industrial
contexts.

利用 SCADA 数据和先进的时间序列方法（FNN 和 LSTM 网络），我们预测不同风力涡轮的发电量，同时提出了集成 FNN 和 LSTM 模型的新方法以提高预测的稳定性和准确性。此外，我们应用机器学习技术来检测风力涡轮性能的退化，从而实现主动维护策略和健康评估。这些研究结果突显了为不同涡轮提供自动化定制的重要性。值得注意的是，该研究方法不仅适用于风力涡轮，还可在各种机械设备中扩展，突出了我们研究的多样性和适用性。

设备健康评估：风力发电机性能的时序分析

Equipment Health Assessment: Time Series Analysis for Wind Turbine  Performance

Detection of rolling-element bearing faults is crucial for implementing
proactive maintenance strategies and for minimizing the economic and
operational consequences of unexpected failures. However, many existing
techniques are developed and tested under strictly controlled conditions,
limiting their adaptability to the diverse and dynamic settings encountered in
practical applications. This paper presents an efficient real-time
convolutional neural network (CNN) for diagnosing multiple bearing faults under
various noise levels and time-varying rotational speeds. Additionally, we
propose a novel Fisher-based spectral separability analysis (SSA) method to
elucidate the effectiveness of the designed CNN model. We conducted experiments
on both healthy bearings and bearings afflicted with inner race, outer race,
and roller ball faults. The experimental results show the superiority of our
model over the current state-of-the-art approach in three folds: it achieves
substantial accuracy gains of up to 15.8%, it is robust to noise with high
performance across various signal-to-noise ratios, and it runs in real-time
with processing durations five times less than acquisition. Additionally, by
using the proposed SSA technique, we offer insights into the model's
performance and underscore its effectiveness in tackling real-world challenges.

该研究论文提出了一种高效的实时卷积神经网络（CNN）来诊断多个滚动轴承故障，针对不同噪声水平和时变转速的实际应用环境。并且，使用一种新颖的基于 Fisher 的频谱可分离性分析（SSA）方法，阐明了所设计 CNN 模型的有效性。实验证实了该模型在三个方面优于最先进方法：精度提高高达 15.8%，在各种信噪比下具有噪声鲁棒性，且实时处理速度是采集速度的五分之一。此外，通过所提供的 SSA 技术，我们揭示了该模型在应对实际挑战方面的有效性。