Next Article in Journal
Fabrication of a SnO2-Based Acetone Gas Sensor Enhanced by Molecular Imprinting
Next Article in Special Issue
An Integrated Backscatter Ultrasound Technique for the Detection of Coronary and Carotid Atherosclerotic Lesions
Previous Article in Journal
Surface Plasmon Resonator Using High Sensitive Resonance Telecommunication Wavelengths for DNA Sensors of Mycobacterium Tuberculosis with Thiol-Modified Probes
Previous Article in Special Issue
A Novel Ultrasound Technique for Detection of Osteochondral Defects in the Ankle Joint: A Parametric and Feasibility Study
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(1), 341-351; doi:10.3390/s150100341

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

1
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
State key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Received: 29 October 2014 / Accepted: 16 December 2014 / Published: 26 December 2014
View Full-Text   |   Download PDF [998 KB, uploaded 26 December 2014]   |  

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. View Full-Text
Keywords: rolling bearing; piezoelectric ultrasonic transducer; wavelet packet entropy flow manifold learning; bearing factory quality evaluation rolling bearing; piezoelectric ultrasonic transducer; wavelet packet entropy flow manifold learning; bearing factory quality evaluation
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Chen, X.; Liu, D.; Xu, G.; Jiang, K.; Liang, L. Application of Wavelet Packet Entropy Flow Manifold Learning in Bearing Factory Inspection Using the Ultrasonic Technique. Sensors 2015, 15, 341-351.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top