An Improved Scheme for Vibration-Based Rolling Bearing Fault Diagnosis Using Feature Integration and AdaBoost Tree-Based Ensemble Classifier

An Improved Scheme for Vibration-Based Rolling Bearing Fault Diagnosis Using Feature Integration and AdaBoost Tree-Based Ensemble Classifier

Bearings are key components in modern power machines. Effective diagnosis of bearing faults is crucial for normal operation. Recently, the deep convolutional neural network (DCNN) with 2D visualization technology has shown great potential in bearing fault diagnosis. Traditional DCNN-based fault diag...

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Bibliographic Details
Main Authors: Bingxi Zhao, Qi Yuan, Hongbin Zhang
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Applied Sciences
Subjects:
rolling bearing
fault diagnosis
deep convolutional neural network
adaptive boosting algorithm
Online Access:https://www.mdpi.com/2076-3417/10/5/1802

Internet

https://www.mdpi.com/2076-3417/10/5/1802

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