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首页> 外文期刊>IEEE transactions on industrial informatics >Joint Learning of Degradation Assessment and RUL Prediction for Aeroengines via Dual-Task Deep LSTM Networks
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Joint Learning of Degradation Assessment and RUL Prediction for Aeroengines via Dual-Task Deep LSTM Networks

机译：通过双任务深度LSTM网络联合学习航空发动机的退化评估和RUL预测

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摘要

Health assessment and prognostics are two key tasks within the prognostics and health management frame of equipment. However, existing works are performing these two tasks separately and hierarchically. In this paper, we design and establish dual-task deep long short-term memory networks for joint learning of degradation assessment and remaining useful life prediction of aeroengines. This enables a more robust and accurate assessment and prediction results making for the increment of operational reliability and safety as well as maintenance cost reduction. Meanwhile, the target label functions that match the network training are constructed in an adaptive way according to the health state of an individual aeroengine. Experiments on the popular C-MAPSS lifetime dataset of aeroengines are employed to verify the accuracy and effectiveness. The performance of our proposed work exhibits superiority over other state-of-the-art approaches and demonstrate its application potential.
机译：健康评估和预测是设备预测和健康管理框架中的两个关键任务。但是，现有的作品正在分别和分层地执行这两项任务。在本文中，我们设计并建立了双任务深长期短期记忆网络，用于联合学习航空发动机的退化评估和剩余使用寿命。这使评估和预测结果更可靠，更准确，从而提高了操作的可靠性和安全性，并降低了维护成本。同时，根据单个航空发动机的健康状况，以自适应方式构造与网络训练匹配的目标标签功能。通过对流行的航空发动机C-MAPSS寿命数据集进行实验，以验证其准确性和有效性。我们提出的工作表现出优于其他最新方法的优势，并展示了其应用潜力。

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