Planetary gearbox fault feature learning using conditional variational neural networks under noise environment

The features signals of early fault collected from planetary gearbox are usually weak. It is difficult to extract effective fault features from the collected vibration signals under noise environment. In this paper, a new feature learning method for fault diagnosis of planetary gearbox based on deep...

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Veröffentlicht in:	Knowledge-based systems 2019-01, Vol.163, p.438-449
Hauptverfasser:	Wang, You-ren, Jin, Qi, Sun, Guo-dong, Sun, Can-fei
Format:	Artikel
Sprache:	eng
Schlagworte:	Deep conditional variational neural networks Fault diagnosis Feature extraction Feature learning Frequency spectrum Gearboxes Learning Multilayers Neural networks Noise reduction Normal distribution Planetary gearbox Resampling Signal to noise ratio Vibration
Online-Zugang:	Volltext
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