Abstract 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.

中文翻译：

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具有非生命周期标记数据集的用于 RUL 预测的平均 Bi-LSTM 网络

摘要 LSTM 网络是一种有效的 RNN 模型，可用于预测系统 RUL，因为它在顺序数据处理方面具有优越的性能。通常，由生命周期标记数据集训练的网络将具有相似的 RUL 预测精度，因为网络训练算法可以确保整个训练数据集的网络最优性。然而，对于由非生命周期标记样本训练的网络，不同训练条件引起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，这些不确定的局部点计算出的RUL预测结果可能会出现较大的差异。因此，为了使用由非生命周期标记样本训练的网络获得准确的 RUL 预测，我们的论文提出了一种新的网络模型平均方法来处理网络不确定性。更重要的是，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，使用石墨/LiCoO2 电池的退化值来验证所提出方法的有效性。结果表明，所提出的方法能够提高RUL预测精度，减少预测误差。Graphite/LiCoO2 电池的退化值用于验证所提出方法的有效性。结果表明，所提出的方法能够提高RUL预测精度，减少预测误差。Graphite/LiCoO2 电池的退化值用于验证所提出方法的有效性。结果表明，所提出的方法能够提高RUL预测精度，减少预测误差。