Next Article in Journal
Pedestrian Detection in Far-Infrared Daytime Images Using a Hierarchical Codebook of SURF
Previous Article in Journal
A Label-Free Immunosensor for Ultrasensitive Detection of Ketamine Based on Quartz Crystal Microbalance
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(4), 8550-8569; doi:10.3390/s150408550

EEMD Independent Extraction for Mixing Features of Rotating Machinery Reconstructed in Phase Space

1
School of Mechanical Engineering, Xi'an Jiaotong University, No.28 Xianning West Road, Xi'an 710049, China
2
State Key Lab for Strength and Vibration of Mechanical Structures, Xi'an Jiaotong University, Xi'an 710049, China
3
School of Mechanical Engineering, Xinjiang University, No.1043 Yanan Road, Wulumuqi 830047, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 25 December 2014 / Revised: 31 March 2015 / Accepted: 31 March 2015 / Published: 13 April 2015
(This article belongs to the Section Physical Sensors)

Abstract

Empirical Mode Decomposition (EMD), due to its adaptive decomposition property for the non-linear and non-stationary signals, has been widely used in vibration analyses for rotating machinery. However, EMD suffers from mode mixing, which is difficult to extract features independently. Although the improved EMD, well known as the ensemble EMD (EEMD), has been proposed, mode mixing is alleviated only to a certain degree. Moreover, EEMD needs to determine the amplitude of added noise. In this paper, we propose Phase Space Ensemble Empirical Mode Decomposition (PSEEMD) integrating Phase Space Reconstruction (PSR) and Manifold Learning (ML) for modifying EEMD. We also provide the principle and detailed procedure of PSEEMD, and the analyses on a simulation signal and an actual vibration signal derived from a rubbing rotor are performed. The results show that PSEEMD is more efficient and convenient than EEMD in extracting the mixing features from the investigated signal and in optimizing the amplitude of the necessary added noise. Additionally PSEEMD can extract the weak features interfered with a certain amount of noise. View Full-Text
Keywords: PSEEMD; EEMD; mode mixing; amplitude of added noise; weak features PSEEMD; EEMD; mode mixing; amplitude of added noise; weak features
Figures

Figure 1

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

Ma, Z.; Wen, G.; Jiang, C. EEMD Independent Extraction for Mixing Features of Rotating Machinery Reconstructed in Phase Space. Sensors 2015, 15, 8550-8569.

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