Abstract Change of working condition leads to discrepancy in domain distribution of equipment vibration signals. This discrepancy poses an obstacle to application of deep learning method in fault diagnosis of wind turbine. When lacking domain adaptation ability, diagnostic accuracy of deep learning method applied to unseen condition will decrease significantly. To solve this problem, an iterative matching network augmented with selective sample reuse strategy is proposed. By generating pseudo labels for unlabeled signals from unseen condition and reusing these signals to iteratively update parameters, embedding space of matching network reduce discrepancy in domain distribution between different working conditions. This makes the model more adaptable to unseen condition. Specially designed filter is proposed for selecting pseudo-labeled signals to increase proportion of correctly labeled signals in iteration. By combing these two points, proposed algorithm can be updated iteratively based on selected pseudo-labeled signals and achieve higher accuracy when analyzing signals of unseen working conditions. Multiscale feature extractor is used to extract features at different scales and form embedding space. Effectiveness of the proposed algorithm is verified by four datasets. Experiments show that this algorithm not only has good performance under varying load and speed conditions but also surpasses other domain adaptation methods.

中文翻译：

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基于选择性信号重用策略增强的迭代匹配网络对不同工况下机械设备的智能故障诊断

摘要 工况变化导致设备振动信号域分布的差异。这种差异对深度学习方法在风力发电机组故障诊断中的应用构成了障碍。当缺乏领域适应能力时，深度学习方法在未知条件下的诊断准确率会显着下降。为了解决这个问题，提出了一种增加了选择性样本重用策略的迭代匹配网络。通过从看不见的条件为未标记的信号生成伪标签并重用这些信号来迭代更新参数，匹配网络的嵌入空间减少了不同工作条件之间域分布的差异。这使得模型更适应看不见的条件。提出了专门设计的滤波器来选择伪标记信号，以增加迭代中正确标记信号的比例。通过结合这两点，所提出的算法可以基于选定的伪标记信号进行迭代更新，并在分析看不见的工作条件的信号时达到更高的精度。多尺度特征提取器用于提取不同尺度的特征并形成嵌入空间。四个数据集验证了所提出算法的有效性。实验表明，该算法不仅在变化的负载和速度条件下具有良好的性能，而且优于其他域自适应方法。所提出的算法可以根据选定的伪标记信号进行迭代更新，并在分析未知工作条件的信号时实现更高的准确性。多尺度特征提取器用于提取不同尺度的特征并形成嵌入空间。四个数据集验证了所提出算法的有效性。实验表明，该算法不仅在变化的负载和速度条件下具有良好的性能，而且优于其他域自适应方法。所提出的算法可以根据选定的伪标记信号进行迭代更新，并在分析未知工作条件的信号时实现更高的准确性。多尺度特征提取器用于提取不同尺度的特征并形成嵌入空间。四个数据集验证了所提出算法的有效性。实验表明，该算法不仅在变化的负载和速度条件下具有良好的性能，而且优于其他域自适应方法。