Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to improve its reliability and availability, and reduce its maintenance costs. This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the RUL prediction of the AEs. In this method, several kinds of DBRNNs with different neuron structures are built to extract hidden features from sensory data. A new customized loss function is designed to evaluate the performance of the DBRNNs, and a series of the RUL values is obtained. Then, these RUL values are reencapsulated into a predicted RUL domain. By updating the weights of elements in the domain, multiple regression decision tree (RDT) models are trained iteratively. These models integrate the predicted results of different DBRNNs to realize the final RUL prognostics with high accuracy. The proposed method is validated by using C-MAPSS datasets from NASA. The experimental results show that the proposed method has achieved more superior performance compared with other existing methods.

中文翻译：

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用于飞机发动机剩余使用寿命预测的深度双向循环神经网络集成


航空发动机（AE）的剩余使用寿命（RUL）预测对于提高其可靠性和可用性、降低其维护成本具有重要意义。本文提出了一种新颖的深度双向循环神经网络 (DBRNN) 集成方法，用于 AE 的 RUL 预测。在该方法中，构建了几种具有不同神经元结构的 DBRNN，以从传感数据中提取隐藏特征。设计了一种新的定制损失函数来评估 DBRNN 的性能，并获得了一系列 RUL 值。然后，这些 RUL 值被重新封装到预测的 RUL 域中。通过更新域中元素的权重，迭代训练多元回归决策树（RDT）模型。这些模型整合了不同 DBRNN 的预测结果，以实现最终的高精度 RUL 预测。所提出的方法通过使用 NASA 的 C-MAPSS 数据集进行了验证。实验结果表明，与其他现有方法相比，该方法取得了更为优越的性能。