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题名: Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications
作者: Lin, Yanhui; Li, Xudong; Hu, Yang
作者部门: 空间技术部
通讯作者: Hu, Yang (yang.hu@polimi.it)
刊名: Applied Soft Computing Journal
ISSN号: 1568-4946
出版日期: 2018
收录类别: EI
英文摘要: 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 L1-norm penalty) and Extreme Learning Machines (trained by considering the L2-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. © 2018 Elsevier B.V.
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.nssc.ac.cn/handle/122/6186
Appears in Collections:空间技术部_期刊论文

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