Application of EMD and 1.5-dimensional spectrum in fault feature extraction of rolling bearing

Abstract

The original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bearing is proposed, which combines empirical mode decomposition (EMD) with a 1.5-dimensional spectrum. The original signal is decomposed by EMD to obtain the intrinsic modal function (IMF) of different scales. The IMF is selected by the size of a correlation coefficient and a kurtosis value to eliminate the high-frequency components, which is reconstructed to achieve the purpose of noise reduction. The reconstructed Hilbert envelope signal is analysed by the 1.5-dimensional spectrum to extract the nonlinear characteristic of two phase couplings, so that the fault characteristic frequency of the bearing is obtained. By analysing the signal of actual rolling bearings, the fault characteristic frequency of bearing inner and outer rings can be effectively extracted, and the validity and feasibility of the method are proved.

Keywords

• feature extraction
• machine bearings
• hilbert transforms
• mechanical engineering computing
• rolling bearings
• fault diagnosis
• vibrations
• signal processing
• outer rings
• bearing inner rings
• actual rolling bearings
• fault characteristic frequency
• hilbert envelope signal
• high-frequency components
• fault information
• phase coupling components
• bearing fault
• rolling bearing
• fault feature extraction
• 1.5-dimensional spectrum
• emd