The Fault Diagnosis of Rolling Bearing based on MED and HHT
- 10.2991/lemcs-15.2015.53How to use a DOI?
- Rolling bearing; Optimal filter; Minimum entropy deconvolution; HHT; Fault Diagnosis
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 makes it be 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.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhidong Wang AU - Rui Huo AU - Daokun Zhang PY - 2015/07 DA - 2015/07 TI - The Fault Diagnosis of Rolling Bearing based on MED and HHT BT - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science PB - Atlantis Press SP - 278 EP - 282 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.53 DO - 10.2991/lemcs-15.2015.53 ID - Wang2015/07 ER -