A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions
Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-based prognostic methods still lack an effective model that can utilize operational conditions data for remaining useful life (RUL) prediction. This paper develop...
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| Published in | IEEE transactions on industrial electronics (1982) Vol. 66; no. 11; pp. 8792 - 8802 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-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 AI-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. |
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| AbstractList | Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-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 AI-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. |
| Author | Huang, Cheng-Geng Li, Yan-Feng Huang, Hong-Zhong |
| Author_xml | – sequence: 1 givenname: Cheng-Geng orcidid: 0000-0001-8538-7248 surname: Huang fullname: Huang, Cheng-Geng email: cheng-geng.huang@hotmail.com organization: Center for System Reliability and Safety, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Hong-Zhong orcidid: 0000-0003-4478-8349 surname: Huang fullname: Huang, Hong-Zhong email: hzhuang@uestc.edu.cn organization: Center for System Reliability and Safety, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 3 givenname: Yan-Feng surname: Li fullname: Li, Yan-Feng email: yanfengli@uestc.edu.cn organization: Center for System Reliability and Safety, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China |
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| Snippet | Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-based prognostic... |
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| SubjectTerms | Aircraft engine Aircraft engines Artificial intelligence Data models data-driven prognostic deep learning (DL) Engines Feature extraction long short-term memory Methods prognostic and health management remaining useful life (RUL) estimation Sensors Testing Training Turbofan engines |
| Title | A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions |
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