Conventional intelligent diagnostic model is built on the foundation that the training data and testing data are recorded under the same operating condition, which neglects the fact that the operating condition of the rotating machinery usually varies. The feature distribution of the recorded data in one operating condition may be inconsistent with the feature distribution of the recorded data in another operating condition. It is easy to cause a significant distribution discrepancy between the training data and testing data. To address this issue, an unsupervised domain adaptation approach based on a symmetric co-training framework is proposed in this study. In the proposed symmetric co-training framework, a universal feature extractor and two individual classifiers are built as the main elements. The structures of the two classifiers are symmetric and its parameters are updated in a co-training style. The parameters of the feature extractor and two classifiers are continuously updated via an adversarial training process. The cosine similarity of the predictions from two classifiers is introduced to guide the adversarial training process, which can not only minimize the distribution discrepancies between source domain data and target domain data, but also push the feature subspaces for different healthy conditions away from the class boundaries. The application of the proposed method on two sets of experimental bearing fault data validates that the proposed method can successfully address the domain shift phenomenon between the recorded data under different operating conditions.

中文翻译：

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基于对称协同训练的滚动轴承智能故障诊断无监督域自适应方法

传统的智能诊断模型建立在训练数据和测试数据记录在同一工况下的基础上，忽略了旋转机械工况通常会发生变化的事实。记录数据在一种操作条件下的特征分布可能与另一种操作条件下记录数据的特征分布不一致。很容易造成训练数据和测试数据之间存在显着的分布差异。为了解决这个问题，本研究提出了一种基于对称协同训练框架的无监督域适应方法。在所提出的对称协同训练框架中，构建了一个通用特征提取器和两个单独的分类器作为主要元素。两个分类器的结构是对称的，其参数以协同训练的方式更新。特征提取器和两个分类器的参数通过对抗训练过程不断更新。引入两个分类器预测的余弦相似度来指导对抗训练过程，这不仅可以最小化源域数据和目标域数据之间的分布差异，还可以将不同健康状况的特征子空间推离类边界. 将所提出的方法应用于两组实验轴承故障数据，验证了所提出的方法可以成功解决不同工况下记录数据之间的域偏移现象。特征提取器和两个分类器的参数通过对抗训练过程不断更新。引入两个分类器预测的余弦相似度来指导对抗训练过程，这不仅可以最小化源域数据和目标域数据之间的分布差异，还可以将不同健康状况的特征子空间推离类边界. 将所提出的方法应用于两组实验轴承故障数据，验证了所提出的方法可以成功解决不同工况下记录数据之间的域偏移现象。特征提取器和两个分类器的参数通过对抗训练过程不断更新。引入两个分类器预测的余弦相似度来指导对抗训练过程，这不仅可以最小化源域数据和目标域数据之间的分布差异，还可以将不同健康状况的特征子空间推离类边界. 将所提出的方法应用于两组实验轴承故障数据，验证了所提出的方法可以成功解决不同工况下记录数据之间的域偏移现象。引入两个分类器预测的余弦相似度来指导对抗训练过程，这不仅可以最小化源域数据和目标域数据之间的分布差异，还可以将不同健康状况的特征子空间推离类边界. 将所提出的方法应用于两组实验轴承故障数据，验证了所提出的方法可以成功解决不同工况下记录数据之间的域偏移现象。引入两个分类器预测的余弦相似度来指导对抗训练过程，这不仅可以最小化源域数据和目标域数据之间的分布差异，还可以将不同健康状况的特征子空间推离类边界. 将所提出的方法应用于两组实验轴承故障数据，验证了所提出的方法可以成功解决不同工况下记录数据之间的域偏移现象。