论文标题
通过保存隐私的联合学习,范围范围内的风力涡轮机状况信息共享
Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning
论文作者
论文摘要
风力涡轮机制造商每天都从其机队收集数据。然而,缺乏数据访问和共享会阻碍数据的全部潜力。我们提出了一种分布式的机器学习方法,该方法通过将数据留在风力涡轮机上,同时仍可以在这些本地数据上学习,从而保留了数据隐私。我们表明,通过联合车队的学习,几乎没有代表性培训数据的涡轮机可以从更准确的正常行为模型中受益。在被监视的目标变量在整个车队上分布的情况下,将全球联合模型定制为单个涡轮机可以产生最高的故障检测准确性。我们为轴承温度证明了这一点,该目标变量的正常行为可以根据涡轮机的不同而变化。我们表明,没有涡轮机会因参与联合学习过程而导致模型绩效丧失,从而在我们的案例研究中导致联合学习策略的出色表现。分布式学习将正常行为模型训练时间增加了约10倍,因为沟通开销和较慢的模型收敛。
Terabytes of data are collected by wind turbine manufacturers from their fleets every day. And yet, a lack of data access and sharing impedes exploiting the full potential of the data. We present a distributed machine learning approach that preserves the data privacy by leaving the data on the wind turbines while still enabling fleet-wide learning on those local data. We show that through federated fleet-wide learning, turbines with little or no representative training data can benefit from more accurate normal behavior models. Customizing the global federated model to individual turbines yields the highest fault detection accuracy in cases where the monitored target variable is distributed heterogeneously across the fleet. We demonstrate this for bearing temperatures, a target variable whose normal behavior can vary widely depending on the turbine. We show that no turbine experiences a loss in model performance from participating in the federated learning process, resulting in superior performance of the federated learning strategy in our case studies. The distributed learning increases the normal behavior model training times by about a factor of ten due to increased communication overhead and slower model convergence.