Application of wavelet packet entropy flow manifold learning in bearing factory inspection using the ultrasonic technique

Xiaoguang Chen; Dan Liu; Guanghua Xu; Kuosheng Jiang; Lin Liang

doi:10.3390/s150100341

Application of wavelet packet entropy flow manifold learning in bearing factory inspection using the ultrasonic technique

Sensors (Basel). 2014 Dec 26;15(1):341-51. doi: 10.3390/s150100341.

Authors

Xiaoguang Chen¹, Dan Liu², Guanghua Xu³, Kuosheng Jiang⁴, Lin Liang⁵

Affiliations

¹ School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. chenxiaoguang2008@163.com.
² School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. liudan1978@mail.xjtu.edu.cn.
³ School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. ghxu@mail.xjtu.edu.cn.
⁴ School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. jiangkuosheng333@stu.xjtu.edu.cn.
⁵ School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. lianglin@mail.xjtu.edu.cn.

Abstract

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.