Next Article in Journal
Average Dielectric Property Analysis of Complex Breast Tissue with Microwave Transmission Measurements
Previous Article in Journal
A Fiber-Tip Label-Free Biological Sensing Platform: A Practical Approach toward In-Vivo Sensing
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(1), 1182-1198; doi:10.3390/s150101182

Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum

School of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
*
Author to whom correspondence should be addressed.
Received: 9 October 2014 / Accepted: 5 January 2015 / Published: 9 January 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1049 KB, uploaded 9 January 2015]   |  

Abstract

The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. View Full-Text
Keywords: fast NL-Means; envelop spectrum; fault diagnosis; rolling bearing fast NL-Means; envelop spectrum; fault diagnosis; rolling bearing
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

Lv, Y.; Zhu, Q.; Yuan, R. Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum. Sensors 2015, 15, 1182-1198.

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