Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis

Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis

In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous fea...

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Bibliographic Details
Main Authors:	Yan Chu, Syed Muhammad Ali, Mingfeng Lu, Yanan Zhang
Format:	Article
Language:	English
Published:	MDPI AG 2023-08-01
Series:	Entropy
Subjects:	heterogeneous features random subspace method bearing fault diagnosis deep stack autoencoder lasso
Online Access:	https://www.mdpi.com/1099-4300/25/8/1194

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