As a popular renewable energy generation technology, wind turbine system has become a critical enabler for building the sustainable cyber-physical system (CPS). The main shaft bearing is an important part of the wind turbine CPS and often runs under variable working conditions. Thus, the reliable bearing diagnosis method can timely discover the main shaft bearing fault, which reduces the maintenance cost of wind turbines. Inspired by the idea of domain adaptation, we combined domain adversarial neural network and residual network and proposed a novel deep domain adversarial residual neural network (DDA-RNN) for diagnosing bearing fault and improving model performance on the unlabeled dataset. This proposed software and hardware co-design method was evaluated by our bearing dataset, which was collected from two wind turbine CPSs from Sanmenxia in Henan Province. Besides, F1 score and accuracy are served as model metrics, which reflect the diagnosis performance. Compared with other methods, the experimental results show that DDA-RNN can improve model performance. Meanwhile, DDA-RNN extracts diagnosis knowledge from labeled dataset and improves the model performance on the unlabeled dataset under different working condition. Therefore, the proposed method can be potentially used to benefit many practical scenarios in the future.

中文翻译：

---

用于可持续风力涡轮机信息物理系统故障诊断的深域对抗残差神经网络

作为一种流行的可再生能源发电技术，风力涡轮机系统已成为构建可持续信息物理系统（CPS）的关键推动因素。主轴轴承是风力发电机CPS的重要组成部分，经常在变工况下运行。因此，可靠的轴承诊断方法可以及时发现主轴轴承故障，从而降低风电机组的维护成本。受域自适应思想的启发，我们将域对抗性神经网络和残差网络相结合，提出了一种新的深度域对抗性残差神经网络（DDA-RNN），用于在未标记的数据集上诊断轴承故障并提高模型性能。我们的轴承数据集对提出的软硬件协同设计方法进行了评估，采集自河南省三门峡的两台风力发电机 CPS。此外，F1 分数和准确率作为模型指标，反映了诊断性能。与其他方法相比，实验结果表明DDA-RNN可以提高模型性能。同时，DDA-RNN 从标记数据集中提取诊断知识，提高模型在不同工作条件下在未标记数据集上的性能。因此，所提出的方法可以潜在地用于在未来使许多实际场景受益。DDA-RNN 从标记数据集中提取诊断知识，提高模型在不同工作条件下在未标记数据集上的性能。因此，所提出的方法可以潜在地用于在未来使许多实际场景受益。DDA-RNN 从标记数据集中提取诊断知识，提高模型在不同工作条件下在未标记数据集上的性能。因此，所提出的方法可以潜在地用于在未来使许多实际场景受益。