Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume th...

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Bibliographic Details
Main Authors: Gaowei Xu, Min Liu, Zhuofu Jiang, Dirk Söffker, Weiming Shen
Format: Article
Language:English
Published: MDPI AG 2019-03-01
Series:Sensors
Subjects:
bearing fault diagnosis
convolutional neural network (CNN)
random forest (RF)
continuous wavelet transform (CWT)
ensemble learning
Online Access:http://www.mdpi.com/1424-8220/19/5/1088

Internet

http://www.mdpi.com/1424-8220/19/5/1088

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