Convolutional Neural Network Based Bearing Fault Diagnosis

被引：31

作者：

Duy-Tang Hoang ^{[1
]}

Kang, Hee-Jun ^{[2
]}

机构：

[1] Univ Ulsan, Grad Sch Elect Engn, Ulsan 680749, South Korea

[2] Univ Ulsan, Sch Elect Engn, Ulsan 680749, South Korea

来源：

INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT II | 2017年 / 10362卷

基金：

新加坡国家研究基金会;

关键词：

Convolutional neural network; Deep learning; Bearing fault diagnosis; Signal based fault diagnosis; FEATURES;

D O I：

10.1007/978-3-319-63312-1_9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a new bearing fault diagnosis method without the feature extraction, based on Convolutional Neural Network (CNN). The 1-D vibration signal is converted to 2-D data called vibration image. Then, the vibration images are fed into the CNN for bearing fault classification. Experiments are carried out with bearing data from the Case Western Reserve University Bearing Fault Database and its result are compared with the results of other methods to show the effectiveness of the proposed algorithm.

引用

页码：105 / 111

页数：7

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