The bearing fault diagnosis of rotating machinery by using Hilbert-Huang transform

摘要

Based on improve the drawbacks of Ensemble Empirical Mode Decomposition (EEMD), such as mode mixing and end effect problem, post-processing of EEMD which was improved with HHT approach to solve the problem in this paper. Once the Intrinsic Mode Functions (IMFs) are obtained from the decomposition process, the crucial step is to extract the fault features from the information-contained IMFs. The amplitude modulation (AM) phenomenon can be discovered in the IMFs with fault information. In this paper, we not only classify the types of bearing fault but also identify the level of the fault.