相关技术在滚动轴承故障诊断中的应用

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Rolling Bearing Fault Detection Using Correlation Technique

摘要: 众所周知自相关技术能够有效地从随机嘈声中提取周期性信号.在滚动轴承元件的故障诊断中,由于信号图象不同,我们无法从原信号直接得到故障的情况,这种信号的显示就像宽频带随机嘈声在自相关函数的显示一样.本文中,信号经过了预处理,其结果证明是行之有效的.应用自相关技术我们还可以得到已测定的可比采样,这对于建立轴承运行条件的数据基础和进行故障诊断是十分重要的.
- 自相关函数 /
- 波带参数 /
- 冲击参数 /
- 峰值参数 /
- 峰态参数
Abstract: It's known that auto-correlation technique is effective in extracting periodical signals from random noises. In the case of fault monitoring of rolling element bearing, we can't acquire the fault information directly from the original signal because of the difference of signal phases. And the signal is shown as the wide band random signal in auto-correlation function. In this paper, the signal is pre-processed and the results are proved effective. Moreover, by taking the auto-correlation function we can obtain the determined and comparable samples. This is very important for establishing the data base of running condition and for detecting the faults.
- auto-correlation function /
- wave shape factor /
- crest factor /
- impulse factor /
- kurtosis factor

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