Machine learning approaches work well with large labeled data sets. In the field of fault diagnosis, the need to analyze large amounts of data provides a foundation for machine learning to be applied. However, due to the changes of rotation speed, load and other factors, data sets will be of different data distribution. Therefore, under the condition of different feature and different feature distributions, it is of great importance to improve the generalization ability of machine learning model. In this paper, the deep convolutional neural network and the principle of parameter transfer are used to extract features and transfer parameters of rolling bearing data samples under different working conditions. This paper proposes to train the machine learning model on relevant data sets with different feature distributions, and improve the learning effect of the model under other conditions by means of parameter transfer. Different working conditions are simulated by using the data collected from different experimental tables, and the transfer effects between different data collected from different experimental tables are discussed respectively for different damage degrees and different rotating speeds. Experimental results show that, compared with direct verification, this method can effectively improve the performance of models under different working conditions.

中文翻译：

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基于参数传递的各种工况下旋转机械故障诊断的跨域学习

机器学习方法适用于大型标记数据集。在故障诊断领域，分析大量数据的需求为机器学习的应用提供了基础。但是，由于转速、负载等因素的变化，数据集会有不同的数据分布。因此，在不同特征、不同特征分布的情况下，提高机器学习模型的泛化能力就显得尤为重要。本文利用深度卷积神经网络和参数传递原理，提取滚动轴承数据样本在不同工况下的特征和传递参数。本文提出在具有不同特征分布的相关数据集上训练机器学习模型，并通过参数传递的方式提高模型在其他条件下的学习效果。利用不同实验表采集的数据模拟了不同工况，分别讨论了不同实验表采集的不同数据在不同损伤程度和不同转速下的传递效应。实验结果表明，与直接验证相比，该方法可以有效提高模型在不同工况下的性能。分别讨论了不同实验表中不同数据在不同损伤程度和不同转速下的传递效应。实验结果表明，与直接验证相比，该方法可以有效提高模型在不同工况下的性能。分别讨论了不同实验表中不同数据在不同损伤程度和不同转速下的传递效应。实验结果表明，与直接验证相比，该方法可以有效提高模型在不同工况下的性能。