| Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications | |
| Lin, Yanhui1; Li, Xudong2; Hu, Yang3 | |
| Department | 空间技术部 |
| Source Publication | APPLIED SOFT COMPUTING
 |
| 2018 | |
| Volume | 72Pages:555-564 |
| DOI | 10.1016/j.asoc.2018.01.036 |
| ISSN | 1568-4946 |
| Language | 英语 |
| Keyword | Feature learning Auto-encoder Extreme learning machines Prognostics and health management Motor bearing Turbofan engine |
| Abstract | Prognostics and Health Management (PHM) is an integrated technique for improving the availability and efficiency of high-value industry equipment and reducing the maintenance cost. One of the most challenging problems in PHM is how to effectively process the raw monitoring signal into the information-rich features that are readable enough for PHM modeling. In this paper, we propose an integrated hierarchical learning framework, which is capable to perform the unsupervised feature learning, diagnostics and prognostics modeling together. The proposed method is based on Auto-Encoders (trained by considering the Li-norm penalty) and Extreme Learning Machines (trained by considering the L-2-norm penalty). The proposed method is applied on two different case studies considering the diagnostics of motor bearings and prognostics of turbofan engines, also the performances are compared with other commonly applied PHM approaches and machine learning tools. The obtained results demonstrate the superiority of the proposed method, especially the ability of extracting the relevant features from the non-informative and noisy signals and maintaining their efficiencies. (C) 2018 Elsevier B.V. All rights reserved. |
| Indexed By | SCI ; EI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.nssc.ac.cn/handle/122/6558 |
| Collection | 空间技术部 |
| Affiliation | 1.School of Reliability and Systems Engineering, Beihang University, Beijing, China; 2.National Space Science Center, Beijing, China; 3.Science and Technology on Complex Aviation System Simulation Laboratory, Beijing,9236, China |
| Recommended Citation GB/T 7714 | Lin, Yanhui,Li, Xudong,Hu, Yang. Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications[J]. APPLIED SOFT COMPUTING,2018,72:555-564. |
| APA | Lin, Yanhui,Li, Xudong,&Hu, Yang.(2018).Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications.APPLIED SOFT COMPUTING,72,555-564. |
| MLA | Lin, Yanhui,et al."Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications".APPLIED SOFT COMPUTING 72(2018):555-564. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 201872555-564.pdf（1977KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | Application Full Text | |
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