Next Article in Journal
DBN Structure Design Algorithm for Different Datasets Based on Information Entropy and Reconstruction Error
Next Article in Special Issue
Rotor Fault Diagnosis Based on Characteristic Frequency Band Energy Entropy and Support Vector Machine
Previous Article in Journal
Geometry of Thermodynamic Processes
Previous Article in Special Issue
A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering
Article Menu

Export Article

Open AccessArticle
Entropy 2018, 20(12), 926; https://doi.org/10.3390/e20120926

Rolling Element Bearing Fault Diagnosis by Combining Adaptive Local Iterative Filtering, Modified Fuzzy Entropy and Support Vector Machine

1
School of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China
2
College of Energy and Power Engineering, Dalian University of Technology, Dalian 116023, China
*
Author to whom correspondence should be addressed.
Received: 11 November 2018 / Revised: 29 November 2018 / Accepted: 1 December 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
Full-Text   |   PDF [1794 KB, uploaded 4 December 2018]   |  

Abstract

A new fault feature extraction method for rolling element bearing is put forward in this paper based on the adaptive local iterative filtering (ALIF) algorithm and the modified fuzzy entropy. Due to the bearing vibration signals’ non-stationary and nonlinear characteristics, the ALIF method, which is a new approach for the analysis of the non-stationary signals, is used to decompose the original vibration signals into a series of mode components. Fuzzy entropy (FuzzyEn) is a nonlinear dynamic parameter for measuring the signals’ complexity. However, it only emphasizes the signals’ local characteristics while neglecting its global fluctuation. Considering the global fluctuation of bearing vibration signals will change with the bearing working condition varying, we modified the FuzzyEn. The modified FuzzyEn (MFuzzyEn) of the first few modes obtained by the ALIF is utilized to form the fault feature vectors. Subsequently, the corresponding feature vectors are input into the multi-class SVM classifier to accomplish the bearing fault identification automatically. The experimental analysis demonstrates that the presented ALIF-MFuzzyEn-SVM approach can effectively recognize the different fault categories and different levels of bearing fault severity. View Full-Text
Keywords: adaptive local iterative filtering; modified fuzzy entropy; SVM; rolling element bearing; fault diagnosis adaptive local iterative filtering; modified fuzzy entropy; SVM; rolling element bearing; fault diagnosis
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

Zhu, K.; Chen, L.; Hu, X. Rolling Element Bearing Fault Diagnosis by Combining Adaptive Local Iterative Filtering, Modified Fuzzy Entropy and Support Vector Machine. Entropy 2018, 20, 926.

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