Next Article in Journal
Backtracking and Mixing Rate of Diffusion on Uncorrelated Temporal Networks
Previous Article in Journal
Kovacs-Like Memory Effect in Athermal Systems: Linear Response Analysis
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Entropy 2017, 19(10), 541; https://doi.org/10.3390/e19100541

Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

1
Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 234 0000, Chile
2
Centro de Investigación y Modelamiento de Fenómenos Aleatorios de Valparaíso (CIMFAV), Universidad de Valparaíso, Valparaíso 234 0000, Chile
*
Author to whom correspondence should be addressed.
Received: 7 September 2017 / Revised: 5 October 2017 / Accepted: 9 October 2017 / Published: 13 October 2017
(This article belongs to the Section Information Theory)
Full-Text   |   PDF [402 KB, uploaded 13 October 2017]   |  

Abstract

The behavioural diagnostics of bearings play an essential role in the management of several rotation machine systems. However, current diagnostic methods do not deliver satisfactory results with respect to failures in variable speed rotational phenomena. In this paper, we consider the Shannon entropy as an important fault signature pattern. To compute the entropy, we propose combining stationary wavelet transform and singular value decomposition. The resulting feature extraction method, that we call stationary wavelet singular entropy (SWSE), aims to improve the accuracy of the diagnostics of bearing failure by finding a small number of high-quality fault signature patterns. The features extracted by the SWSE are then passed on to a kernel extreme learning machine (KELM) classifier. The proposed SWSE-KELM algorithm is evaluated using two bearing vibration signal databases obtained from Case Western Reserve University. We compare our SWSE feature extraction method to other well-known methods in the literature such as stationary wavelet packet singular entropy (SWPSE) and decimated wavelet packet singular entropy (DWPSE). The experimental results show that the SWSE-KELM consistently outperforms both the SWPSE-KELM and DWPSE-KELM methods. Further, our SWSE method requires fewer features than the other two evaluated methods, which makes our SWSE-KELM algorithm simpler and faster. View Full-Text
Keywords: stationary wavelet singular entropy; singular value decomposition; kernel extreme learning machine stationary wavelet singular entropy; singular value decomposition; kernel extreme learning machine
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

Share & Cite This Article

MDPI and ACS Style

Rodriguez, N.; Cabrera, G.; Lagos, C.; Cabrera, E. Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis. Entropy 2017, 19, 541.

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]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top