Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

被引:22
|
作者
Li, Rui [1 ]
Ran, Chao [1 ]
Zhang, Bin [2 ,3 ]
Han, Leng [2 ]
Feng, Song [2 ]
机构
[1] Chongqing Univ Posts & Telecommun, Sch Automat, Chongqing 400065, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Sch Adv Mfg Engn, Chongqing 400065, Peoples R China
[3] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 16期
关键词
fault diagnosis; rolling bearing; nonlinear entropy; improved complete ensemble empirical mode decomposition with adaptive noise; ensemble SVM; rotating machines; DISPERSION ENTROPY; CLASSIFICATION; SPECTRUM;
D O I
10.3390/app10165542
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.
引用
收藏
页数:18
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