Title
Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests.
Abstract
This study presents a two-step fault diagnosis scheme combined with statistical classification and random forests-based classification for rolling element bearings. Considering the inequality of features sensitivity in different diagnosis steps, the proposed method utilizes permutation entropy and variational mode decomposition to depict vibration signals under single scale and multiscale. In the first step, the permutation entropy features on the single scale of original signals are extracted and the statistical classification model based on Chebyshev's inequality is constructed to detect the faults with a preliminary acquaintance of the bearing condition. In the second step, vibration signals with fault conditions are firstly decomposed into a collection of intrinsic mode functions by using variational mode decomposition and then multiscale permutation entropy features derived from each mono-component are extracted to identify the specific fault types. In order to improve the classification ability of the characteristic data, the out-of-bag estimation of random forests is firstly employed to reelect and refine the original multiscale permutation entropy features. Then the refined features are considered as the input data to train the random forests-based classification model. Finally, the condition data of bearings with different fault conditions are employed to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively identify the working conditions and fault types of rolling element bearings.
Year
DOI
Venue
2019
10.3390/e21010096
ENTROPY
Keywords
Field
DocType
fault diagnosis,rolling element bearing,permutation entropy,variational mode decomposition,statistical classification,random forests
Mathematical optimization,Variational mode decomposition,Permutation entropy,Algorithm,Bearing (mechanical),Rolling-element bearing,Chebyshev filter,Vibration,Statistical classification,Random forest,Mathematics
Journal
Volume
Issue
ISSN
21
1
1099-4300
Citations 
PageRank 
References 
0
0.34
15
Authors
5
Name
Order
Citations
PageRank
Xiaoming Xue111.38
Chaoshun Li226215.91
Suqun Cao300.34
Jinchao Sun400.34
Liyan Liu500.34