One of the significant tasks in remaining useful life (RUL) prediction is to find a good health indicator (HI) that can effectively represent the degradation process of a system. However, it is difficult for traditional data-driven methods to construct accurate HIs due to their incomprehensive consideration of temporal dependencies within the monitoring data, especially for aeroengines working under nonstationary operating conditions (OCs). Aiming at this problem, this article develops a novel unsupervised deep neural network, the so-called times series memory auto-encoder with sequentially updated reconstructions (SUR-TSMAE) to improve the accuracy of extracted HIs, which directly takes the multidimensional time series as input to simultaneously achieve feature extraction from both feature-dimension and time-dimension. Further, to make full use of the temporal dependencies, a novel long-short time memory with sequentially updated reconstructions (SUR-LSTM), which uses the errors not only from the current memory cell but also from subsequent memory cells to update the output layer’s weight of the current memory cell, is developed to act as the reconstructed layer in the SUR-TSMAE. The use of SUR-LSTM can help the SUR-TSMAE rapidly reconstruct the input time series with higher precision. Experimental results on a public dataset demonstrate the outstanding performance of SUR-TSMAE in comparison with some existing methods.

中文翻译：

---

一种新颖的时间序列内存自动编码器，具有顺序更新重建的剩余使用寿命预测功能


剩余使用寿命（RUL）预测的重要任务之一是找到能够有效表征系统退化过程的良好健康指标（HI）。然而，传统的数据驱动方法由于没有全面考虑监测数据中的时间依赖性，因此很难构建准确的HI，特别是对于在非平稳运行条件（OC）下工作的航空发动机。针对这个问题，本文开发了一种新颖的无监督深度神经网络，即所谓的具有顺序更新重建的时间序列记忆自动编码器（SUR-TSMAE）来提高提取HI的准确性，该网络直接将多维时间序列作为输入同时实现特征维度和时间维度的特征提取。此外，为了充分利用时间依赖性，一种新颖的具有顺序更新重建的长短时记忆（SUR-LSTM），它不仅使用当前记忆单元的错误，还使用后续记忆单元的错误来更新输出层的错误当前存储单元的权重，被开发用作 SUR-TSMAE 中的重建层。 SUR-LSTM的使用可以帮助SUR-TSMAE以更高的精度快速重建输入时间序列。公共数据集上的实验结果表明，与一些现有方法相比，SUR-TSMAE 具有出色的性能。