Deep learning (DL) has shown effectiveness in the field of Prognostic and Health Management (PHM) of rolling bearings and successfully solved many problems with its powerful feature learning and function fitting capabilities. However, it is still very challenging to transfer a remaining useful life (RUL) prediction model trained by historical data to bearings running under new operating conditions. Domain adaptation (DA) is an effective solution to obtain cross-domain features of bearings. This article presents an RUL prediction method for rolling bearings based on a deep transfer auto-encoder. Two strategies, parameter transfer and feature representation transfer, are used to transfer the feature extraction model and RUL regression model trained in the labeled source domain to the unlabeled target domain. In this way, the full utilization of the historical model and the rapid adaptation to the new operating conditions are realized. A case study on the IEEE PHM Challenge 2012 bearing data set verifies the effectiveness of the proposed method. The comparison with other nontransfer and transfer machine learning models shows the advantages of this method in precision and accuracy.

中文翻译：

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基于深度传递自动编码器的滚动轴承剩余使用寿命预测新方法

深度学习（DL）在滚动轴承的预测和健康管理（PHM）领域已显示出有效性，并凭借其强大的特征学习和功能拟合功能成功解决了许多问题。但是，将由历史数据训练的剩余使用寿命（RUL）预测模型转移到在新的工作条件下运行的轴承仍然是非常困难的。域自适应（DA）是获得轴承跨域特征的有效解决方案。本文提出了一种基于深度传递自动编码器的滚动轴承RUL预测方法。参数转移和特征表示转移这两种策略用于将在标记源域中训练的特征提取模型和RUL回归模型转移到未标记目标域中。这样，实现了历史模型的充分利用和对新运行条件的快速适应。以IEEE PHM Challenge 2012轴承数据集为例，验证了该方法的有效性。与其他非转移和转移机器学习模型的比较显示了该方法在准确性和准确性方面的优势。