@inproceedings{c0a06c2809d94586a12fcfbdd4711141,
title = "A framework for asset prognostics from fleet data",
abstract = "Prognostics of a specific asset based on data from a fleet of same assets, but operated in different environmental and operational conditions is an important and common problem in Prognostics and Health Management (PHM). Traditional data-driven models trained on all fleet data provide only a general degradation trend, without capturing the specificity of the degradation process of the different assets. A two-step data-driven framework is here proposed to tackle this problem. A general model is trained traditionally on all fleet data and a correction model is built to estimate the deviation of the general model outcome from the degradation process of the specific asset of interest. The proposed framework is tested on a case study concerning the failure of a pneumatic valve in a nuclear power plant. The experimental results show the effectiveness of the proposed two-step, data-driven framework.",
keywords = "Correction model, Fleet, Fuzzy similarity analysis, General model, Prognostics, Support vector machine",
author = "Jie Liu and Enrico Zio",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016 ; Conference date: 19-10-2016 Through 21-10-2016",
year = "2017",
month = jan,
day = "16",
doi = "10.1109/PHM.2016.7819824",
language = "英语",
series = "Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Qiang Miao and Zhaojun Li and Zuo, \{Ming J.\} and Liudong Xing and Zhigang Tian",
booktitle = "Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016",
address = "美国",
}