Rotary machine health diagnosis based on empirical mode decomposition

This paper presents a signal decomposition and feature extraction technique for the health diagnosis of rotary machines, based on the empirical mode decomposition. Vibration signal measured from a defective rolling bearing is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components contained within the vibration signal. Two criteria, the energy measure and correlation measure, are investigated to determine the most representative IMF for extracting defect-induced characteristic features out of vibration signals. The envelope spectrum of the selected IMF is investigated as an indicator for both the existence and the specific location of structural defects within the bearing. Theoretical foundation of the technique is introduced, and its performance is experimentally verified.