摘要
在对滚动轴承微弱故障诊断时,故障信号容易受到噪声的干扰,为了获取滚动轴承数据的有效故障信息,研究用分数阶傅里叶变换(fractional Fourier transform,FRFT)的方法对滚动轴承工作中产生的微弱故障进行诊断。该方法可以将滚动轴承数据变换到分数阶域的空间中进行分析,在此空间中变换分数阶的阶次从而搜索提取出微弱故障的最大峰值,分析结果表明用分数阶傅里叶算法可以有效的降低其他分量和噪声的互相干扰,准确的提取目标分量,实验结果证实了该方法的有效性和可行性。
In fault diagnosis of rolling bearings,the fault signal is easy to be interfered by the ambient noise,Therefore,an approach based on Fractional Fourier Transform（ FRFT） is studied in this research to collect valid data of rolling bearing fault. With utilizing this approach,data can be analyzed by being converted into fractional domain,as well as 3D simulation. Consequently,the fractional can be changed to extract the weak fault to search for the maximum peak of weak fault. According to the analysis,the Fractional Fourier Transform algorithm is able to effectively reduce the mutual interference of other components and noise,and accurately extract the target component. Hence,the research findings are able to prove the validity and feasibility of the approach studied in this paper.
出处
《哈尔滨理工大学学报》
CAS
北大核心
2017年第3期68-72,79,共6页
Journal of Harbin University of Science and Technology
基金
哈尔滨市科技创新人才项目(1014RFQXJ163)
