Empirical mode decomposition of acoustic emission for early detection of bearing defects

Abstract

Empirical Mode Decomposition (EMD) is one of the techniques that proved its efficiency for an early detection of defects in many mechanical applications like bearings and gears. The EMD methodology decomposes the original times series data into intrinsic mode functions (IMF), by using the Hilbert-Huang transform. In this study, EMD is applied to acoustic emission signals. The acoustic emission signal is heterodynined around a central high frequency in order to obtain an audible signal. Scalar statistical parameters such as Kurtosis and THIKAT are then used in this study. These statistical descriptors are calculated for each IMF. The technique is validated by experiments on a test bench with a very small crack (40 lm) on the outer race of a ball bearing. It is found that the application of time descriptors to different IMF decomposition levels gives good results for early detection of defects in comparison with the original time signal.