An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder

Fault diagnosis of rotating machinery is vital to improve the security and reliability as well as avoid serious accidents. For instance, robust fault features are crucial to achieve a high diagnosis precision. However, traditional feature extraction methods rely on an abundant amount of expertise an...

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 76; S. 170 - 184
Hauptverfasser:	Shen, Changqing, Qi, Yumei, Wang, Jun, Cai, Gaigai, Zhu, Zhongkui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.11.2018
Schlagworte:	Contractive autoencoder Correlation analysis Fault diagnosis Robust features Contractive autoencoder Fault diagnosis Robust features Correlation analysis
ISSN:	0952-1976, 1873-6769
Online-Zugang:	Volltext
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