@inproceedings{13515c7dc3504b07a23a6c27331fe0a9,
title = "Fault diagnosis for the intermittent fault in gyroscopes: A data-driven method",
abstract = "In this paper, a data-driven method is proposed to detect and isolate the intermittent fault in gyroscopes. A mathematical description for the intermittent fault is first proposed in a probabilistic framework. Based on this probabilistic model, we present a random generation algorithm to emulate the occurrence of the intermittent fault. Considering the cross-correlation and autocorrelation between the measurement data, this paper proposes a data-driven fault diagnosis method based on dynamic principal component analysis. In the simulation part, both additive intermittent fault and multiplicative intermittent fault scenarios are considered. Simulation results illustrate that the proposed method is able to detect and isolate the intermittent fault effectively.",
keywords = "Data-driven, Fault Diagnosis, Gyroscope, Intermittent Fault",
author = "Liliang Li and Zhenhua Wang and Yi Shen",
note = "Publisher Copyright: {\textcopyright} 2016 TCCT.; 35th Chinese Control Conference, CCC 2016 ; Conference date: 27-07-2016 Through 29-07-2016",
year = "2016",
month = aug,
day = "26",
doi = "10.1109/ChiCC.2016.7554401",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6639--6643",
editor = "Jie Chen and Qianchuan Zhao and Jie Chen",
booktitle = "Proceedings of the 35th Chinese Control Conference, CCC 2016",
address = "美国",
}