@inproceedings{2891b0c67f154ca6a90975de9e85f721,
title = "An operating condition classified prognostics approach for remaining useful life estimation",
abstract = "This paper presents a prognostics approach based on operating condition for estimating the Remaining Useful Life (RUL). Operating condition is used to describe the state or environment of a system. This approach is suit for the dataset that contains sensor measurements and operational settings. Predicting RUL contains two stages: modeling stage using the training dataset and predicting stage using the result of modeling and testing dataset. This approach can increase available information in modeling stage and simulate the actual work situation of the test unit in the predicting stage. The performance of this approach was tested by the dataset from 2008 PHM Data Challenge Competition where sensor measurements and operational settings were provided. The task of the competition was to estimate the RUL of an unspecified system. The results showed that this prognostic method could get accurate predictions in most situations and had a good rank in all competition results.",
keywords = "Data driven, Operating condition, Perfomance degradation, Prognostics, Remaining useful life",
author = "Qi Li and Gao, \{Zhan Bao\} and Shao, \{Li Qun\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Conference on Prognostics and Health Management, PHM 2014 ; Conference date: 22-06-2014 Through 25-06-2014",
year = "2015",
month = feb,
day = "9",
doi = "10.1109/ICPHM.2014.7036396",
language = "英语",
series = "2014 International Conference on Prognostics and Health Management, PHM 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2014 International Conference on Prognostics and Health Management, PHM 2014",
address = "美国",
}