Raw vibration signal pattern recognition with automatic hyper-parameter-optimized convolutional neural network for bearing fault diagnosis

Bearing fault diagnosis is of great significance for evaluating the reliability of machines because bearings are the critical components in rotating machinery and are prone to failure. Because of non-stationarity and the low signal-noise rate of raw vibration signals, traditional fault diagnosis met...

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Veröffentlicht in:	Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science Journal of mechanical engineering science, 2020-01, Vol.234 (1), p.343-360
Hauptverfasser:	Li, Heng, Zhang, Qing, Qin, Xianrong, Yuantao, Sun
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Component reliability Critical components Fault diagnosis Feature extraction Machine learning Neural networks Optimization Pattern recognition Reliability analysis Roller bearings Rotating machinery Vibration
Online-Zugang:	Volltext
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