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计量学报  2022, Vol. 43 Issue (10): 1326-1334    DOI: 10.3969/j.issn.1000-1158.2022.10.13
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一种改进的自适应多元变分模态分解轴承故障信号特征提取方法
时培明1,张慧超1,伊思颖1,韩东颖2
1.燕山大学 河北省测试计量技术及仪器重点实验室,河北 秦皇岛 066004
2.燕山大学 车辆与能源学院,河北 秦皇岛 066004
An Improved Feature Extraction Method of Bearing Fault Signal Based on Adaptive Multivariate Variational Mode Decomposition
SHI Pei-ming1,ZHANG Hui-chao1,YI Si-ying1,HAN Dong-ying2
1. Key Laboratory of Measurement Technology and Instrument of Hebei Province, Yanshan University,Qinhuangdao, Hebei 066004,China 
2. School of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei 066004,China
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摘要 针对实际工程中轴承信号的非线性和非平稳性,提出一种自适应多元变分模态分解算法。多元变分模态的分解效果主要与本征模态数k和惩罚参数α相关,为了解决人为经验参数设置对多元信号分解结果的影响,一种自适应的信号分解算法被提出。具体内容如下:首先将混合灰狼算法与多元变分模态分解算法相结合,提出最小模态重叠分量指标,将其作为适应度函数来寻求(k, α)的最优解,按照最优解对多元信号进行分解,提取故障特征。采用仿真信号和实际数据来验证所提方法的有效性和准确性,通过与多元经验模态分解、级联变分模态分解的对比分析,验证该算法在滚动轴承故障特征提取方面的高效性和实用性。
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时培明
张慧超
伊思颖
韩东颖
关键词 计量学滚动轴承故障诊断多元变分模态分解混合灰狼算法模态重叠分量    
Abstract:Aiming at the nonlinearity and non-stationarity of bearing signals in practical engineering, an adaptive multi-variable mode decomposition algorithm is proposed. The decomposition effect of multivariate variational modes is mainly related to the number of intrinsic modes k and penalty parameter α. In order to solve the influence of artificial empirical parameter setting on the decomposition results of multivariate signals, an adaptive signal decomposition algorithm is proposed. The specific contents are as follows: Firstly, the hybrid gray wolf algorithm is combined with the multivariate variational mode decomposition algorithm, and the minimum mode overlap component index is proposed, which is used as the fitness function to seek the optimal solution of(k, α). According to the optimal solution, the multivariate signals are decomposed and the fault features are extracted. Simulation signals and actual data are used to verify the effectiveness and accuracy of the proposed method. By comparing with multivariate empirical mode decomposition (MEMD) and cascade variational mode decomposition, the effectiveness and practicability of the proposed method in rolling bearing fault feature extraction are verified.
Key wordsmetrology    rolling bearing    fault diagnosis    multivariate variational mode decomposition    hybrid grey wolf optimization algorithm    mode overlap component
收稿日期: 2021-10-12      发布日期: 2022-10-14
PACS:  TB936  
  TB973  
基金资助:国家自然科学基金(61973262);河北省自然科学基金(E2020203147);中央引导地方科技发展资金(216Z4301G, 216Z2102G);河北省引进留学人员资助项目(C20190371)
作者简介: 时培明(1979-),男,黑龙江延寿人,博士,燕山大学教授,主要研究方向为智能信息处理和设备监测诊断。Email: spm@ysu.edu.cn
引用本文:   
时培明,张慧超,伊思颖,韩东颖. 一种改进的自适应多元变分模态分解轴承故障信号特征提取方法[J]. 计量学报, 2022, 43(10): 1326-1334.
SHI Pei-ming,ZHANG Hui-chao,YI Si-ying,HAN Dong-ying. An Improved Feature Extraction Method of Bearing Fault Signal Based on Adaptive Multivariate Variational Mode Decomposition. Acta Metrologica Sinica, 2022, 43(10): 1326-1334.
链接本文:  
http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2022.10.13     或     http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2022/V43/I10/1326
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