The Fault Diagnosis of Rolling Bearing based on MED and HHT

As an essential part of rotating machinery, the early fault diagnosis of rolling bearing can improve the safety of mechanical operation. What's more, the machining accuracy can be effectively ensured. Unfortunately, the early fault signal of rolling bearing is extremely weak, which can be easily covered by others. And the endpoint effect will appear in several Intrinsic Mode functions (IMFs) when it is decomposed by Hilbert-Huang transform (HHT), which make it difficult to find the fault position accurately. To get the real components, the minimum Entropy Deconvolution (MED) method is proposed here to obtain the effective impact components with an inverse filter, which can improve the signal-to-noise ratio. The MED method which can effectively extract the useful information of the rolling bearing's fault signal, and fully suppress the endpoint effect of Empirical Mode decomposition (EMD), as well as greatly improve the ability to precisely find the fault position, which has been confirmed by a lot of experiments, which has a great practical value in the practical production.