Application of Wavelet Packet Entropy Flow Manifold Learning in Bearing Factory Inspection Using the Ultrasonic Technique

被引：17

作者：

Chen, Xiaoguang ^{[1
]}

Liu, Dan ^{[1
]}

Xu, Guanghua ^{[1
,2
]}

Jiang, Kuosheng ^{[1
]}

Liang, Lin ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China

[2] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China

来源：

SENSORS | 2015年 / 15卷 / 01期

基金：

中国国家自然科学基金;

关键词：

rolling bearing; piezoelectric ultrasonic transducer; wavelet packet entropy flow manifold learning; bearing factory quality evaluation; SPECTRAL KURTOSIS; FAULT-DIAGNOSIS;

D O I：

10.3390/s150100341

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

For decades, bearing factory quality evaluation has been a key problem and the methods used are always static tests. This paper investigates the use of piezoelectric ultrasonic transducers (PUT) as dynamic diagnostic tools and a relevant signal classification technique, wavelet packet entropy (WPEntropy) flow manifold learning, for the evaluation of bearing factory quality. The data were analyzed using wavelet packet entropy (WPEntropy) flow manifold learning. The results showed that the ultrasonic technique with WPEntropy flow manifold learning was able to detect different types of defects on the bearing components. The test method and the proposed technique are described and the different signals are analyzed and discussed.

引用

页码：341 / 351

页数：11

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