Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning

Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault diagnosis has some deficiencies, such as complex computation of feature vectors, excessive dependence on prior knowledge and...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 67; H. 1; S. 185 - 195
Hauptverfasser:	Sun, Jiedi, Yan, Changhong, Wen, Jiangtao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Bearing fault Compressed sensing compressed sensing (CS) Data acquisition Deep learning deep neural network (DNN) Dependence Diagnostic systems Fault diagnosis Feature extraction intelligent diagnosis Machine learning Machinery condition monitoring Neural networks Roller bearings Rotating machinery Rotating machines Signal processing stacked sparse autoencoder (SSAE) Time-domain analysis Vibration monitoring
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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