Health prognosis of rolling bearing is of great significance to improve its safety and reliability. This paper presents a novel health prognosis method for the rolling bearing based on convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) model. First, a new nonlinear degradation indicator (DI) is designed which can be utilized as training label. Then, through learning and capturing the mapping relationship between raw vibration signals and DI of the rolling bearing, a CNN model is introduced to estimate the DI value of the rolling bearing. And, BiLSTM models are set up to carry out health prognosis using the estimated DI, including future DI and remaining useful life prediction. An experiment verification is implemented to validate the effectiveness of the proposed method. Results show the excellent ability of future DI prediction, and demonstrate the superiority of the proposed method in the field of remaining useful life prediction compared with other existing deep learning models.

滚动轴承的健康预后对提高其安全性和可靠性具有重要意义。本文提出了一种基于卷积神经网络（CNN）和双向长短期记忆网络（BiLSTM）模型的滚动轴承健康预测方法。首先，设计了一种新的非线性退化指标（DI），可以将其用作训练标签。然后，通过学习并捕获滚动轴承原始振动信号与DI之间的映射关系，引入CNN模型来估计滚动轴承的DI值。并且，BiLSTM模型被设置为使用估计的DI进行健康预测，包括将来的DI和剩余使用寿命预测。实验验证了该方法的有效性。