Improving the Accuracy of Fault Frequency by Means of Local Mean Decomposition and Ratio Correction Method for Rolling Bearing Failure

The fault frequencies are as they are and cannot be improved. One can only improve its estimation quality. This paper proposes a fault diagnosis method by combining local mean decomposition (LMD) and the ratio correction method to process the short-time signals. Firstly, the vibration signal of roll...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2019-05, Vol.9 (9), p.1888
Main Authors: Duan, Yongqiang, Wang, Chengdong, Chen, Yong, Liu, Peisen
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Decomposition
Deep learning
Error analysis
Error correction
Fault diagnosis
Fault lines
Fault location
Fourier transforms
frequency accuracy
Information processing
local mean decomposition
Machinery
Methods
Neural networks
Parameter estimation
ratio correction method
Roller bearings
Signal processing
Spectrum analysis
spectrum correction
Time signals
Wavelet transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The fault frequencies are as they are and cannot be improved. One can only improve its estimation quality. This paper proposes a fault diagnosis method by combining local mean decomposition (LMD) and the ratio correction method to process the short-time signals. Firstly, the vibration signal of rolling bearing is decomposed into a series of product functions (PFs) by LMD. The PF, which contains the richest fault information, is selected to perform envelope spectrum analysis by the Hilbert transform (HT). Secondly, the Hilbert envelope spectrum of the selected PF is corrected with the ratio correction method. Finally, higher precision fault frequencies are extracted from the corrected Hilbert envelope spectrum, and then the fault location is accurately determined. The proposed method of this paper can be used in online real-time monitoring technology of rolling bearing failure.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9091888