@inproceedings{28f776db90854b85b1e8c4b052e2247f,
title = "Multi-domain description method for bearing fault recognition in varying speed condition",
abstract = "Confusion of frequency spectrum under varying speed condition leads to the efficacy degradation of fault recognition for rolling element bearings. Meanwhile, a widely-used method is transforming non-stationary vibration signals into angular domain based on angular re-sampling to reduce influence of speed fluctuation. However, single domain usually cannot describe health information comprehensively. Thus a multi-domain indexes description method is proposed in this paper to improve fault recognition by multi-domain fusion. First the input indexes of multiple domains including time domain, frequency domain, angular domain and order domain are organized to train SOM neural network. Then an optimized fault cognition model is established based on the SOM input planes. Finally, faults are recognized based on multi-domain indexes model and extra complexity indexes are added to enhance recognition of compound faults. Experimental results show that the proposed method can distinguish different faults effectively and have good practical significance.",
keywords = "Angular re-sampling, Fault diagnosis, Multi-domain indexes, SOM neural network, Varying speed",
author = "Zitong Zhou and Jinglong Chen and Yanyang Zi and Xunzhang Chen",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 42nd Conference of the Industrial Electronics Society, IECON 2016 ; Conference date: 24-10-2016 Through 27-10-2016",
year = "2016",
month = dec,
day = "21",
doi = "10.1109/IECON.2016.7793687",
language = "英语",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
pages = "423--428",
booktitle = "Proceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society",
}