期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 66, 期 11, 页码 8792-8802出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2891463
关键词
Aircraft engine; data-driven prognostic; deep learning (DL); long short-term memory; prognostic and health management; remaining useful life (RUL) estimation
资金
- National Natural Science Foundation of China [51405065]
- China Scholarship Council [201706070061]
Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (Aft-based prognostic methods still lack an effective model that can utilize operational conditions data for remaining useful life (RUL) prediction. This paper develops a novel prognostic method based on bidirectional long short-term memory (BLSTM) networks. The method can integrate multiple sensors data with operational conditions data for RUL prediction of engineered systems. The proposed architecture based on BLSTM networks includes three main parts: first, one BLSTM network is used to directly extract features hidden in the multiple raw sensors signals; second, another BLSTM network is employed to learn higher features from operational conditions signals and the learned features from the sensors signals; and, third, fully connected layers and a linear regression layer are stacked to generate the target output of the RUL prediction. Unlike other Al-based prognostic methods, the developed method can simultaneously model both sensors data and operational conditions data in a consolidated framework. The proposed approach is demonstrated through a case study on aircraft turbofan engines, and comparisons with other popular state-of-the-art methods are also presented.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据