@inproceedings{4fb02f6a7cc74f3dbd160108a6581e92,
title = "An imputation method for missing degradation data based on regression analysis and RBF neural network",
abstract = "Missing degradation data is common in accelerated degradation testing and prognostic and health management, which may bring a lot of difficulties for degradation modeling and life prediction, and lead to inaccurate prediction results. In this paper, the regression analysis and RBF neural network algorithm are used to estimate the trend and fluctuation of missing data respectively, and the estimations are combined to handle with the missing degradation data. The proposed method could make the trend and fluctuation of imputation data better fit the observed data. An engineering case study on a microwave component{\textquoteright}s degradation data is conducted to demonstrate the effectiveness of the proposed method.",
author = "Fuqiang Sun and Ning Wang and Ye Fan and Tongmin Jiang",
note = "Publisher Copyright: {\textcopyright} 2017 Taylor \& Francis Group, London.; 27th European Safety and Reliability Conference, ESREL 2017 ; Conference date: 18-06-2017 Through 22-06-2017",
year = "2017",
doi = "10.1201/9781315210469-383",
language = "英语",
isbn = "9781138629370",
series = "Safety and Reliability - Theory and Applications - Proceedings of the 27th European Safety and Reliability Conference, ESREL 2017",
publisher = "CRC Press/Balkema",
pages = "3021--3026",
editor = "Marko Cepin and Radim Bri{\v s}",
booktitle = "Safety and Reliability – Theory and Applications - Proceedings of the 27th European Safety and Reliability Conference, ESREL 2017",
}