RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images

RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images

To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suita...

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Bibliographic Details
Main Authors:	Jianmin Zhou, Lulu Liu, Xiwen Shen, Xiaotong Yang
Format:	Article
Language:	English
Published:	MDPI AG 2023-06-01
Series:	Applied Sciences
Subjects:	RFR GA-BLS parameter search optimal characteristics fault diagnosis rolling bearings
Online Access:	https://www.mdpi.com/2076-3417/13/13/7350

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