| 基于形态奇异值分解和经验模态分解的滚动轴承故障特征提取方法 |
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| 引用本文： | 汤宝平,蒋永华,张详春.基于形态奇异值分解和经验模态分解的滚动轴承故障特征提取方法[J].机械工程学报,2010,46(5). |
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| 作者姓名： | 汤宝平 蒋永华 张详春 |
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| 作者单位： | 1. 重庆大学机械传动国家重点实验室,重庆,400044
2. 中国航空综合技术研究所,北京,100028 |
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| 基金项目： | 国家自然科学基金(50875272,50735008);;国家高技术研究发展计划(863计划,2009AA04Z411)资助项目 |
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| 摘 要： | 针对随机噪声和局部强干扰影响经验模态分解(Empirical mode decomposition,EMD)质量的问题,提出一种形态奇异值分解滤波消噪方法,并将其与EMD相结合形成一种新的故障特征提取方法。该方法首先对原始振动信号进行相空间重构和奇异值分解(Singular value decomposition,SVD),根据奇异值分布曲线确定降噪阶次进行SVD降噪,再形态滤波,最后把消噪后的信号进行EMD分解,利用本征模模态分量(Intrinsic mode function,IMF)提取故障特征信息。对仿真信号和实际轴承故障数据的应用分析表明,该方法能有效地提取轴承故障特征,诊断轴承故障,还可以减少EMD的分解层数和边界效应,提高EMD分解的时效性和精确度。
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| 关 键 词： | 经验模态分解 奇异值分解 形态滤波 故障特征提取 |
Feature Extraction Method of Rolling Bearing Fault Based on Singular Value Decomposition-morphology Filter and Empirical Mode Decomposition |
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| TANG Baoping,JIANG Yonghua,ZHANG Xiangchun.Feature Extraction Method of Rolling Bearing Fault Based on Singular Value Decomposition-morphology Filter and Empirical Mode Decomposition[J].Chinese Journal of Mechanical Engineering,2010,46(5). |
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| Authors: | TANG Baoping JIANG Yonghua ZHANG Xiangchun |
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| Affiliation: | 1.The State Key Laboratory of Mechanical Transmission/a>;Chongqing University/a>;Chongqing 400044/a>;2.China Aero-polytechnology Establishment/a>;Beijing 100028 |
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| Abstract: | Due to the influence caused by random noises and local strong disturbances embedded in signal on empirical mode decomposition (EMD) results,a novel integrated singular value decomposition-morphology filter method is proposed to overcome this shortcoming.And combining with EMD,a feature extraction method is presented.Firstly,reconstruct the original vibration signal in phase space and decompose the attractor track matrix by singular value decomposition (SVD),and then select a reasonable order for noise reduc... |
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| Keywords: | Empirical mode decomposition Singular value decomposition Morphological filtering Fault feature extraction |
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