Next Article in Journal
Prediction of Cone Crusher Performance Considering Liner Wear
Next Article in Special Issue
Sizing Subsurface Defects in Metallic Plates by Square Pulse Thermography Using an Oriented Gradient Map
Previous Article in Journal
A Novel Ropes-Driven Wideband Piezoelectric Vibration Energy Harvester
Previous Article in Special Issue
Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2016, 6(12), 403; doi:10.3390/app6120403

A Novel Mechanical Fault Diagnosis Scheme Based on the Convex 1-D Second-Order Total Variation Denoising Algorithm

The Key Laboratory of Metallurgical Equipment and Control of Education Ministry, Wuhan University of Science and Technology, Wuhan 430081, China
*
Author to whom correspondence should be addressed.
Academic Editors: Gangbing Song and Chuji Wang
Received: 26 October 2016 / Revised: 21 November 2016 / Accepted: 29 November 2016 / Published: 2 December 2016
(This article belongs to the Special Issue Structural Health Monitoring (SHM) of Civil Structures)
View Full-Text   |   Download PDF [5149 KB, uploaded 2 December 2016]   |  

Abstract

Convex 1-D first-order total variation (TV) denoising is an effective method for eliminating signal noise, which can be defined as convex optimization consisting of a quadratic data fidelity term and a non-convex regularization term. It not only ensures strict convex for optimization problems, but also improves the sparseness of the total variation term by introducing the non-convex penalty function. The convex 1-D first-order total variation denoising method has greater superiority in recovering signals with flat regions. However, it often produces undesirable staircase artifacts. Moreover, actual denoising efficacy largely depends on the selection of the regularization parameter, which is utilized to adjust the weights between the fidelity term and total variation term. Using this, algorithms based on second-order total variation regularization and regularization parameter optimization selection are proposed in this paper. The parameter selection index is determined by the permutation entropy and cross-correlation coefficient to avoid the interference by human experience. This yields the convex 1-D second-order total variation denoising method based on the non-convex framework. Comparing with traditional wavelet denoising and first-order total variation denoising, the validity of the proposed method is verified by analyzing the numerical simulation signal and the vibration signal of fault bearing in practice. View Full-Text
Keywords: second-order total variation denoising; convex optimization; non-convex regularization term; permutation entropy; fault diagnosis second-order total variation denoising; convex optimization; non-convex regularization term; permutation entropy; fault diagnosis
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

Yi, C.; Lv, Y.; Dang, Z.; Xiao, H. A Novel Mechanical Fault Diagnosis Scheme Based on the Convex 1-D Second-Order Total Variation Denoising Algorithm. Appl. Sci. 2016, 6, 403.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top