| 滚动轴承振动信号的稀疏表示研究 |
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| 引用本文： | 郭俊锋,郑晓慧,魏兴春.滚动轴承振动信号的稀疏表示研究[J].甘肃科学学报,2014,26(3):91-94. |
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| 作者姓名： | 郭俊锋 郑晓慧 魏兴春 |
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| 作者单位： | 1. 兰州理工大学数字制造技术与应用省部共建教育部重点实验室,甘肃兰州 730050;兰州理工大学机电工程学院,甘肃兰州 730050
2. 兰州理工大学机电工程学院,甘肃兰州,730050 |
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| 基金项目： | 教育部创新团队:有色冶金成套装备及信息集成技术，国家科技重大专项:动梁无滑枕立式铣车复合加工中心 |
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| 摘 要： | 振动信号蕴含着丰富的装备工作信息,信号稀疏表示能够有效地提取信号最本质的特征.以滚动轴承振动信号为对象,对其进行了稀疏表示研究.根据轴承振动信号的频谱结构特点,基于信号自适应展开,构造了基于指数衰减余弦函数的过完备原子库;用粒子群优化算法(PSO)算法依次搜索原子库中与信号最匹配的原子对其进行稀疏表示并进行了仿真.结果表明:所选择的原子库分解信号后的残余信号更小,相似度达0.912 2,能更好地表示信号.
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| 关 键 词： | 指数衰减正弦原子库 PSO算法 轴承振动信号 稀疏分解 |
On Sparse Representation of Rolling Bearings Vibration Signals |
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| GUO Jun-feng,ZHENG Xiao-hui,WEI Xing-chun.On Sparse Representation of Rolling Bearings Vibration Signals[J].Journal of Gansu Sciences,2014,26(3):91-94. |
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| Authors: | GUO Jun-feng ZHENG Xiao-hui WEI Xing-chun |
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| Institution: | GUO Jun-feng ,ZHENG Xiao-hui ,WEI Xing-chun(1. Key Laboratory of Digital Manufacturing Technology and Application Attached to Ministry Lanzhou University of Technology ,Lanzhou 730050, China ; 2. School of Mechatronic Engineering , Lanzhou University of Technology ,Lanzhou 730050 ,China) |
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| Abstract: | As vibration signals contain a wealth of information on working equipment,the sparse represen- tation of the signals can effectively extract the most essential features of the signals, and rolling bearings vibration signals were employed to research sparse representation. According to the spectral structural characteristics of the signals and the adaptive spread of the signals,an over-complete dictionary was established based on the exponential decay cosine function. Then the PSO （particle swarm optimization） algorithm was adopted to search the atoms from the dictionary that best match with the signals and simulate the sparse representation of the atoms. The results show that after signal decomposition residual signals are smaller and the simplicity is 0. 912 2,which can be better used in representing the signals. |
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| Keywords: | Exponential decay sine dictionary PSO algorithm Bearing vibration signals Sparse decomposition |
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