Wu Jun, Hu Kui, Cheng Yiwei, Zhu Haiping, Shao Xinyu, Wang Yuanhang
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, China.
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, China.
ISA Trans. 2020 Feb;97:241-250. doi: 10.1016/j.isatra.2019.07.004. Epub 2019 Jul 8.
Remaining useful life (RUL) prediction is very important for improving the availability of a system and reducing its life cycle cost. This paper proposes a deep long short-term memory (DLSTM) network-based RUL prediction method using multiple sensor time series signals. The DLSTM model fuses multi-sensor monitoring signals for accurate RUL prediction, which is able to discover the hidden long-term dependencies among sensor time series signals through deep learning structure. By grid search strategy, the network structure and parameters of the DLSTM are efficiently tuned using an adaptive moment estimation algorithm so as to realize an accurate and robust prediction. Two various turbofan engine datasets are adopted to verify the performance of the DLSTM model. The experimental results demonstrate that the DLSTM model has a competitive performance in comparison with state-of-the-arts reported in literatures and other neural network models.
剩余使用寿命（RUL）预测对于提高系统可用性和降低其生命周期成本非常重要。本文提出了一种基于深度长短期记忆（DLSTM）网络的RUL预测方法，该方法使用多个传感器时间序列信号。DLSTM模型融合多传感器监测信号以进行准确的RUL预测，它能够通过深度学习结构发现传感器时间序列信号之间隐藏的长期依赖关系。通过网格搜索策略，使用自适应矩估计算法对DLSTM的网络结构和参数进行有效调整，以实现准确且稳健的预测。采用两个不同的涡扇发动机数据集来验证DLSTM模型的性能。实验结果表明，与文献中报道的现有技术和其他神经网络模型相比，DLSTM模型具有竞争力的性能。
