Rolling bearing fault detection using correlation technique
- 73 Downloads
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.
Key wordsauto-correlation function wave shape factor crest factor impulse factor kurtosis factor
Unable to display preview. Download preview PDF.
- Collacott, R. A.,Mechanical Fault Diagnosis and Condition Monitoring, Chapman and Hall, London (1977).Google Scholar
- Randall, R. B., Computer assisted incipient fault detection on rotating and reciprocating machines,Noise and Vibration Control World-wide, sept. (1981), 230–234.Google Scholar
- Shimokuro Taro,The Most Excellent Control Theory and Application of Stochastic Vibration, Astronautical Pub. (1984) (Chinese version)Google Scholar
- Dyer, D. and R. M. Stewart, Detection of rolling element bearing damage by statistical vibration analysis,ASME Journal of Mechanical Design,100, 2 (1978), 229–235.Google Scholar
- Toyoda Toshio,The Method of Diagnosing Equipment on Working-Spot, Mechanical Industry Pub. (1983). (Chinese version)Google Scholar
- Qu Liang-sheng,In Fault Diagnosis of Machinary, Shanghai Science and Tech. pub. (1986) (in Chinese)Google Scholar