Averaged Bi-LSTM networks for RUL prognostics with non-life-cycle labeled dataset
Averaged Bi-LSTM networks for RUL prognostics with non-life-cycle labeled dataset
复制标题
使用非生命周期标记数据集进行 RUL 预测的平均 Bi-LSTM 网络
DOI：
10.1016/j.neucom.2020.03.041
复制
发表时间：
2020-08
期刊：
影响因子：
6
通讯作者：
Zhang Jianxun
中科院分区：
文献类型：
--
作者：
Yu Yong;Hu Changhua;Si Xiaosheng;Zheng Jianfei;Zhang Jianxun
LSTM网络具有较好的序列数据处理性能，是预测系统规则值的有效RNN模型。通常，使用生命周期标记数据集训练的网络具有相似的RUL预测精度，因为网络训练算法可以保证整个训练数据集的网络最优性。然而，对于使用非生命周期标记样本训练的网络，不同训练条件导致的网络不确定性可能导致某些局部点的退化预测不确定性。此外，由这些不确定的局部点计算得到的RUL预测结果可能会有较大的差异。因此，为了利用非生命周期标记样本训练的网络获得准确的RUL预测，本文提出了一种新的网络模型平均方法来处理网络的不确定性。此外，为了充分学习训练样本的时间相关信息，我们采用Bi-LSTM网络来说明所提出的网络模型平均方法的应用。最后，利用石墨/LiCoO2电池的降解值验证了所提方法的有效性。结果表明，该方法可以提高RUL预测精度，减小预测误差。
LSTM network is an effective RNN model to predict the system RUL for its superior performance in sequential data processing. Usually, networks trained by life-cycle labeled dataset would possess similar RUL predicting accuracies, because the network training algorithm could ensure the network optimality for the whole training dataset. However, for networks trained by non-life-cycle labeled samples, the network uncertainty caused by different training conditions could lead to degradation prediction uncertainty for some local points. Further, the RUL predicting results that are computed by these uncertain local points may shows relatively large differences. Therefore, in order to obtain an accurate RUL prediction with networks trained by non-life-cycle labeled samples, our paper proposes a novel network model averaging method to deal with the network uncertainty. What is more, to learn the temporal correlation information of training samples sufficiently, we adopt the Bi-LSTM network to illustrate the application of the proposed network model averaging method. Finally, degradation values of Graphite/LiCoO2 battery are used to verify the effectiveness of the proposed method. The results show that the proposed method could improve the RUL prediction accuracy and reduce the prediction error.
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