| 最优共振带提取与1.5维谱的滚动轴承早期故障诊断方法 |
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| 引用本文： | 郭俊,黄慧杰,王新,王洪波.最优共振带提取与1.5维谱的滚动轴承早期故障诊断方法[J].机械设计与制造,2019(5):178-181. |
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| 作者姓名： | 郭俊 黄慧杰 王新 王洪波 |
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| 作者单位： | 内蒙古机电职业技术学院机电工程系,内蒙古 呼和浩特,010070;内蒙古科技大学机械工程学院,内蒙古 包头,014010;内蒙古农业大学职业技术学院车辆工程技术系,内蒙古 包头,014109;内蒙古农业大学机电工程学院,内蒙古 呼和浩特,010018 |
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| 基金项目： | 内蒙古自治区高等学校科学研究项目 |
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| 摘 要： | 滚动轴承的振动信号反映到频谱图中,会出现共振带,能够有效并准确提取共振带加以分析是滚动轴承故障诊断常用方法。为了准确提取出共振带,采用巴特沃斯带通滤波器对共振频带进行提取,为了得到最优共振带,将采用特征频率强度系数这一指标来反映提取的共振带效果,然后利用具有高强降噪特性的1.5维谱来对滤波信号进行特征提取.通过仿真信号以及试验信号对该方法进行验证,结果表明,该方法能够在强噪背景下对特征的提取以及实现滚动轴承早期故障诊断。
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| 关 键 词： | 滚动轴承 早期故障 共振带 特征频率强度系数 1.5维谱 |
The Method Optimal Resonance Band Extraction and 1.5 Dimensional Spectrum for Early Fault Diagnosis of Rolling Bearings |
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| GUO Jun,HUANG Hui-jie,WANG Xin,WANG Hong-bo.The Method Optimal Resonance Band Extraction and 1.5 Dimensional Spectrum for Early Fault Diagnosis of Rolling Bearings[J].Machinery Design & Manufacture,2019(5):178-181. |
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| Authors: | GUO Jun HUANG Hui-jie WANG Xin WANG Hong-bo |
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| Affiliation: | (Department of Mechanical and Electrical Engineering,Inner Mongolia Mechanical and Electrical Career Technical College,Inner Mongolia Hohhot 010070,China;Institute of Mechanical Engineering,Inner Mongolia University of Science and Technology,Inner Mongolia Baotou 014010,China;Department of Vehicle Engineering Technology,Career Technical College,Inner Mongolia Agricultural University,Inner Mongolia Baotou 014109,China;College of Mechanical and Electrical Engineering,Inner Mongolia Agricultural University,Inner Mongolia Hohhot 010018,China) |
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| Abstract: | The vibration signals of the rolling bearing are reflected in the spectrum,and there will be resonance bands. It is a common method for the fault diagnosis of rolling bearing to analyze the resonance band effectively and accurately. In order to extract the resonance band accurately,the Butterworth filter is used to extract the resonance band. In order to obtain the optimal resonance band,the characteristic frequency strength coefficient is used to reflect the effect of the extracted resonance band. Then the 1.5 dimensional spectrum with high strength noise reduction characteristic is used to feature the filtered signal.The method is verified by the simulation signal and the test signal. The result shows that the method can extract the features in the strong noise background and realize the early fault diagnosis of the rolling bearing. |
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| Keywords: | Rolling Bearing Early Failure Resonance Band Characteristic Frequency Strength Coefficient 1 5 Dimensional Spectrum |
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