Good prognostic health management (PHM) plays a crucial role in industrial production and other fields. The accurate prediction of remaining useful life (RUL) can ensure good working condition of machines, and the selection of health indicators (HI) is the key to prediction. Due to the length and noise of the data, the selection of features requires a lot of prior knowledge. Therefore, a novel convolution-based attention mechanism bidirectional long and short-term memory (CABLSTM) network is proposed to achieve the end-to-end lifetime prediction of rotating machinery in this paper. Unlike concatenating two networks, the model in this paper is used to convolute the cell states of Bi-LSTM. Firstly, the input signal is performed through CNN to obtain feature information. Secondly, the obtained features are fed into the Bi-LSTM network with attention mechanism for convolution operation to obtain time-frequency information to construct HI. Finally, the training data are normalized to predict RUL. Bi-LSTM can capture features in longer time-frequency information, and attention mechanism can give input influence weight, and highlight its effective characteristics to obtain better prediction accuracy. The complex process of feature extraction, HI construction, and RUL prediction is combined in one algorithm by deep learning. To verify the performance of the method in this paper, experiments were conducted on the bearing dataset of PRONOSTIA, and compared with other methods. The results showed that the method outperforms other methods due to its better accuracy and prediction precision.

良好的预后健康管理（PHM）在工业生产和其他领域中起着至关重要的作用。准确预测剩余使用寿命（RUL）可以确保机器的良好工作状态，而健康指标（HI）的选择是进行预测的关键。由于数据的长度和噪声，特征的选择需要很多先验知识。因此，本文提出了一种基于卷积的新型注意力机制双向长短时记忆（CABLSTM）网络，以实现旋转机械的端到端寿命预测。与串联两个网络不同，本文中的模型用于对Bi-LSTM的单元状态进行卷积。首先，通过CNN执行输入信号以获得特征信息。第二，利用注意力机制将获得的特征馈入Bi-LSTM网络中进行卷积运算，以获得时频信息以构造HI。最后，将训练数据标准化以预测RUL。Bi-LSTM可以捕获较长时频信息中的特征，而注意力机制可以赋予输入影响权重，并突出其有效特征以获得更好的预测精度。通过深度学习将特征提取，HI构建和RUL预测的复杂过程组合在一种算法中。为了验证本文方法的有效性，对PRONOSTIA的轴承数据集进行了实验，并与其他方法进行了比较。结果表明，该方法具有更好的准确性和预测精度，优于其他方法。