Next Article in Journal
A Meliorated Multi-Frequency Band Pyroelectric Sensor
Previous Article in Journal
User Expectations for Media Sharing Practices in Open Display Networks
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(7), 16225-16247; doi:10.3390/s150716225

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

1
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
2
School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
3
College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 27 March 2015 / Revised: 28 June 2015 / Accepted: 1 July 2015 / Published: 6 July 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [689 KB, uploaded 8 July 2015]   |  

Abstract

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. View Full-Text
Keywords: high-dimensional data; fault diagnosis; feature extraction; dimensionality reduction; manifold learning; statistical locally linear embedding high-dimensional data; fault diagnosis; feature extraction; dimensionality reduction; manifold learning; statistical locally linear embedding
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

Wang, X.; Zheng, Y.; Zhao, Z.; Wang, J. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding. Sensors 2015, 15, 16225-16247.

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