Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/72237
Title: Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults
Authors: Fei, C
Choy, YS 
Bai, GC
Tang, WZ
Keywords: Rolling element bearing
Process fault recognition
Information entropy
Multi-feature entropy distance method
Issue Date: 2018
Publisher: SAGE Publications
Source: Structural health monitoring, 2018, v. 17, no. 2, p. 156-168 How to cite?
Journal: Structural health monitoring 
Abstract: To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.
URI: http://hdl.handle.net/10397/72237
ISSN: 1475-9217
EISSN: 1741-3168
DOI: 10.1177/1475921716687167
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

4
Citations as of Apr 3, 2019

WEB OF SCIENCETM
Citations

4
Last Week
0
Last month
Citations as of Apr 9, 2019

Page view(s)

21
Last Week
0
Last month
Citations as of May 21, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.