Fault Feature Extraction of Rolling Element Bearing Based on Improved EMD and Sliding Kurtosis Algorithm
ZHANG Zhigang1 SHI Xiaohui1 CHEN Zheming1 TANG Baoping2
1. Key Laboratory of Manufacture and Test Techniques for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China; 2. The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
Abstract： Considering the characteristics of strong noise of rolling element bearing fault signal, a rolling element bearing fault feature extraction method was proposed based on improved EMD and sliding kurtosis algorithm. Original fault signal was decomposed by EMD to get a finite number of stationary intrinsic mode functions(IMFs). Then mutual information and general correlation coefficient were together used to get rid of pseudo-components in the traditional EMD results, and Real IMF component was processed by sliding kurtosis algorithm to obtain a sliding kurtosis time series. Finally, frequency spectrum of the time series was calculated by Fourier transform to extract the fault feature frequency. Experimental results show that the method can effectively extract the fault feature of rolling element bearing, and is more effective than direct sliding kurtosis algorithm and traditional envelope demodulation in fault feature extraction.
2012, Vol. 31 
