For the data-driven remaining useful life (RUL) prediction for rolling bearings, the traditional machine learning-based methods generally provide insufficient feature representation and adaptive extraction. Although deep learning-based RUL prediction methods can solve these problems to some extent, they still do not yield satisfactory predictive results due to less degradation data and inconsistent data distribution among different bearings. To solve these problems, a new RUL prediction method based on deep feature representation and transfer learning is proposed in this paper. This method includes an off-line stage and an online stage. In the off-line stage, the Hilbert–Huang transform marginal spectra of the raw vibration signal of auxiliary bearings are first calculated as the input, and then contractive denoising autoencoder is introduced to extract deep features with good and stable fault representation. Second, by using the obtained deep features and Pearson’s correlation coefficient, a new health condition assessment method is proposed to divide the whole life of each bearing into a normal state and a fast-degradation state. Finally, using the extracted deep features and their RUL values, an RUL prediction model for the fast-degradation state is trained by means of a least-square support vector machine. In the online stage, a kind of transfer learning algorithm, i.e., transfer component analysis, is introduced to sequentially adjust the features of target bearing from auxiliary bearings, and then the corresponding RUL is predicted using the corrected features. Results using the PHM Challenging 2012 data set show a significant performance improvement when using the proposed method in terms of predictive accuracy and numerical stability.

中文翻译：

---

基于深度特征表示和迁移学习预测滚动轴承的剩余使用寿命

对于滚动轴承的数据驱动剩余使用寿命（RUL）预测，传统的基于机器学习的方法通常无法提供足够的特征表示和自适应提取。虽然基于深度学习的 RUL 预测方法可以在一定程度上解决这些问题，但由于退化数据较少，不同轴承之间的数据分布不一致，它们仍然不能产生令人满意的预测结果。针对这些问题，本文提出了一种基于深度特征表示和迁移学习的RUL预测新方法。该方法包括离线阶段和在线阶段。在离线阶段，首先计算辅助轴承原始振动信号的希尔伯特-黄变换边缘谱作为输入，然后引入收缩去噪自编码器以提取具有良好和稳定故障表示的深层特征。其次，利用获得的深度特征和皮尔逊相关系数，提出了一种新的健康状况评估方法，将每个轴承的整个寿命划分为正常状态和快速退化状态。最后，使用提取的深度特征及其 RUL 值，通过最小二乘支持向量机训练快速退化状态的 RUL 预测模型。在在线阶段，引入一种传递学习算法，即传递分量分析，从辅助轴承依次调整目标轴承的特征，然后利用校正后的特征预测相应的RUL。