FAULT DIAGNOSIS OF ROLLING BEARING BASED ON LEARNING SAMPLE SELECTION VIA CORRELATION ENERGY FLUCTUATION EVALUATION AND DEEP BELIEF NEURAL NETWORK (MT)

FAULT DIAGNOSIS OF ROLLING BEARING BASED ON LEARNING SAMPLE SELECTION VIA CORRELATION ENERGY FLUCTUATION EVALUATION AND DEEP BELIEF NEURAL NETWORK (MT)

The data-driven intelligent diagnosis of rolling bearing status suffers from low recognition rate due to the poor quality of learning samples in the process of identification model construction. To address this problem, a method is proposed to improve the recognition rate of the rolling bearing inte...

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Bibliographic Details
Main Authors: QIN Bo, LUO QuanYi, FENG WeiWei, ZHANG Peng, ZHAO ZhenHua, LI ZiXian, WANG Zhuo
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Strength 2023-01-01
Series:Jixie qiangdu
Subjects:
Learning sample screening
Correlation energy fluctuation evaluation
Rolling bearing
Deep belief neural network
Fault recognition rate
Online Access:http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.02.002
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