Next Article in Journal
Information Geometry of Spatially Periodic Stochastic Systems
Previous Article in Journal
Time–Energy and Time–Entropy Uncertainty Relations in Nonequilibrium Quantum Thermodynamics under Steepest-Entropy-Ascent Nonlinear Master Equations
Article Menu

Export Article

Open AccessArticle

A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition

School of Hydropower & Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(7), 680; https://doi.org/10.3390/e21070680
Received: 20 June 2019 / Revised: 7 July 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
(This article belongs to the Section Signal and Data Analysis)
  |  
PDF [2510 KB, uploaded 15 July 2019]
  |  

Abstract

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches. View Full-Text
Keywords: fault diagnosis; rolling bearing; fast ensemble empirical mode decomposition; refined composite multiscale dispersion entropy; max-relevance and min-redundancy; random forest fault diagnosis; rolling bearing; fast ensemble empirical mode decomposition; refined composite multiscale dispersion entropy; max-relevance and min-redundancy; random forest
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

Zhang, W.; Zhou, J. A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition. Entropy 2019, 21, 680.

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