基于互相关奇异值分解的滚动轴承故障诊断
On the Cross Correlation Singular Value Decomposition Based Fault Diagnosis of Rolling Bearings
  
中文关键词:滚动轴承  故障诊断  奇异值分解  互相关
英文关键词:rolling bearing  fault diagnosis  singular value decomposition  cross correlation
基金项目:
作者单位
乔国鼎 中铁十九局集团第六工程有限公司江苏无锡214000 
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中文摘要:
      滚动轴承在机械装置中非常重要,其运行状态与整台机械设备的工作状态有直接的关系,但在早期弱故障检测时,特征信号经常被淹没在噪声中。为了提高该故障特征的识别精度,提出了基于互相关奇异值分解的故障诊断方法。首先利用奇异值分解将轴承故障信号分解为多个分量信号;其次使用峭度值作为衡量标准,选择两个合适的奇异值分量用于互相关包络分析以获得包络谱;最后通过信号的频谱分析,得到轴承的故障频率,从而完成早期微弱故障检测。通过仿真信号和滚动轴承内圈故障实测数据仿真对比,验证了该方法的有效性。
英文摘要:
      Rolling bearings always play an important part in mechanical equipment. The running state of rolling bearings are directly related to the working state of the whole set of mechanical equipment.However, in the earlier fault detection, the characteristic signal is often drowned in the noise. In order to improve the recognition accuracy of the fault features, a cross correlation singular value decomposition based fault diagnosis method is proposed here in the paper. According to the above mentioned method, the faulty bearing signal is firstly decomposed into multiple component signals by singular value decomposition. Secondly, the kurtosis value is used as the criterion to select two suitable singular value components for cross correlation envelope analysis so as to obtain the envelope spectrum. Finally, the spectrum analysis of the signal is carried out to obtain the fault frequency of the bearing so as to complete the early weak fault detection. The effectiveness of the method is verified by comparing the simulation signals with the measured fault data of the inner ring of the rolling bearing.
乔国鼎.基于互相关奇异值分解的滚动轴承故障诊断[J].国防交通工程与技术,2019,17(6):28~31,44
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