Rolling Bearing Feature Extraction Method Based on Improved Intrinsic Time-Scale Decomposition and Mathematical Morphological Analysis

被引：9
|
作者：
Ma, Jianpeng [1 ]
Chen, Guodong [2 ]
Li, Chengwei [1 ]
Zhan, Liwei [3 ]
Zhang, Guang-Zhu [4 ]
机构：
[1] Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin 150001, Peoples R China
[2] Inner Mongolia Test Inst Aerosp Dynam Machine, Hohhot 010076, Peoples R China
[3] China Harbin Bearing Co LTD, Aero Engine Corp, Harbin 150500, Peoples R China
[4] Catholic Univ Korea, Undergrad Coll, Songsim Global Campus, Bucheon Si 14662, South Korea
来源：
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 06期
关键词：
intrinsic time-scale decomposition; mathematical morphological analysis; feature extraction; rolling bearing; fault diagnosis; FAULT FEATURE-EXTRACTION; DIAGNOSIS; ENVELOPE; HYBRID; FILTER; NOISE; EMD;
D O I：
10.3390/app11062719
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction of rolling bearings.
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收藏
页数：26
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