| 基于最优 IMF 分量和 K-SVD 的滚动轴承故障声音信号特征提取 |
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| 引用本文： | 梁雄鹤,陈珊,魏豪,张丽洁,权伟.基于最优 IMF 分量和 K-SVD 的滚动轴承故障声音信号特征提取[J].机械与电子,2022,40(2):8-12. |
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| 作者姓名： | 梁雄鹤 陈珊 魏豪 张丽洁 权伟 |
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| 作者单位： | 西安工程大学机电工程学院,陕西 西安 710048 |
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| 基金项目： | 陕西省自然科学基础研究计划(2019JQ-852)； |
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| 摘 要： | 针对滚动轴承声音信号中周期性冲击故障特征难提取的问题,提出了基于最优 IMF 分量与 K-SVD 字典学习相结合的轴承故障特征提取方法。首先,利用 VMD 分解原始信号获得一系列 IMF 分量;其次,利用 SAF 指标自适应选取最优 IMF 分量,并作为训练信号;最后,利用 K-SVD 字典学习方法训练出字典库,通过正交匹配追踪算法( OMP )对原始信号处理得到稀疏信号,并对稀疏信号进行包络谱分析。仿真及实验结果表明,对比传统 K-SVD 字典学习方法,该方法得到的稀疏信号信噪比( SNR )更高,能更准确地提取滚动轴承周期性冲击,增强了轴承故障特征。
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| 关 键 词： | 声音信号 SAF 指标 最优 IMF 分量 K-SVD 信噪比 |
Extraction of Rolling Bearing Fault Sound Signal Features Based on Optimal IMFComponents and KSVD |
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| LIANG Xionghe,CHEN Shan,WEI Hao,ZHANG Lijie,QUAN Wei.Extraction of Rolling Bearing Fault Sound Signal Features Based on Optimal IMFComponents and KSVD[J].Machinery & Electronics,2022,40(2):8-12. |
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| Authors: | LIANG Xionghe CHEN Shan WEI Hao ZHANG Lijie QUAN Wei |
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| Affiliation: | ( School of Mechanical and Electrical Engineering , Xi ’ an Polytechnic University , Xi ’ an 710048 , China ) |
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| Abstract: | For the problem of difficult extraction of periodic pulse fault features in rolling bearing sound signals,a bearing fault feature extraction method based on the combination of optimal modal components and KSVD(KSingular value decomposition)dictionary learning is proposed.First,the original signal is decomposed by VMD to obtain a series of IMF components;second,the optimal IMF components are adaptively selected by using SAF(a spectral amplification factor)index as the training signal;finally,the dictionary library is trained by KSVD dictionary learning method,and the sparse signal is obtained by processing the original signal with Orthogonal Matching Pursuit(OMP)and is analyzed by envelope spectrum.Simulation and experimental results show that,compared with the traditional KSVD dictionary learning method,the sparse signaltonoise ratio(SNR)obtained by this method is higher,which can extract the rolling bearing periodic pulse more accurately and enhance the bearing fault characteristics. |
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| Keywords: | sound signal SAF index optimal IMF components KSVD SNR |
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