@inproceedings{eb3a5b89cac84739867f9b6db702e409,
title = "Gear Fault Diagnosis Based on Recurrence Network",
abstract = "Gear is one of the most important components in rotary machine systems. The vibration signals generated from gearbox show strong nonlinearity or chaotic behavior. To identify the complex nonlinear behavior of gear faults, recurrence network is introduced to extract features of gear vibration under different conditions. Quantitative characteristics (such as mean degree centrality, global clustering coefficient, or assortativity of the recurrence network) related to the dynamical complexity of a time series are chosen to help classify the different faults. Experimental study on four different gear conditions has proved that the recurrence network provides a good alternative approach to characterize gear fault.",
keywords = "fault diagnosis, nonlinear time series, recurrence network, rotary machine",
author = "Jing Meng and Liye Zhao and Ruqiang Yan",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 ; Conference date: 16-08-2017 Through 18-08-2017",
year = "2017",
month = dec,
day = "9",
doi = "10.1109/SDPC.2017.103",
language = "英语",
series = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "514--517",
editor = "Wei Guo and \{de Oliveira\}, \{Jose Valente\} and Chuan Li and Yun Bai and Ping Ding and Juanjuan Shi",
booktitle = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",
}