Fault Diagnosis of Rolling Bearing Based on MED and Morphological Filtering

Shang Kun Liu; Gui Ji Tang; Bin Pang

doi:10.4028/www.scientific.net/AMR.989-994.3220

Paper Titles

Separating the Time Field Signal of Rotor Unbalance Vibration Based on EMD Method
p.3204

Application of Rapid Prototyping Technology in Product Design
p.3208

AHP Effectiveness Evaluation of Electronic Warfare Command and Control System under Complex Electromagnetic Environment
p.3212

Design of Quadrotor’s Control System Based on DSP
p.3216

Fault Diagnosis of Rolling Bearing Based on MED and Morphological Filtering
p.3220

Transform a Mechanical Testing Machine into High-Temperature Disk Spring Stress Relaxation Test Equipment
p.3224

Vista of Silt Brick Cutting Machine Development Situation
p.3228

The Design of the Single-Phase Inverter Based on DSP (TMS320F2812)
p.3231

LDO of High Power Supply Rejection with Two-Stage Error Amplifiers and Buffer Compensation
p.3236

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Fault Diagnosis of Rolling Bearing Based on MED...

Fault Diagnosis of Rolling Bearing Based on MED and Morphological Filtering

Abstract:

One rolling bearing fault diagnosis method based on minimum entropy deconvolution (MED) and morphological filtering is proposed. Firstly, the strong background noise of rolling bearings is decreased by the MED method, then the morphological filtering which have different length of structure elements is designed and applied to the de-noised signal. Subsequently, bearing’s fault characteristic frequency is extracted by the amplitude spectrum analysis to the best morphological filtering component which have the maximum kurtosis. This method is used to analyze both a simulated bearing signal and an experimental inner ring fault signal, and the good result validated the effectiveness of this method.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

3220-3223

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.3220

Citation:

Cite this paper

Online since:

July 2014

Authors:

Shang Kun Liu*, Gui Ji Tang, Bin Pang

Keywords:

Fault Diagnosis, Minimum Entropy Deconvolution, Morphological Filtering, Rolling Bearing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Hu Aijun, Tang Guiji, An Liansuo. De-nosing technology for vibration signals of rotating machinery based on morphology filter[J]. Chinese Journal of Mechanical Engineering, 2006, 42(4): 127-130.

DOI: 10.3901/jme.2006.04.127

Google Scholar

[2] Zhang Lijun, Yang Debin, etc. Approach to extracting gear fault feature based on mathematical morphological filtering [J]. Chinese Journal of Mechanical Engineering, 2007, 43(2): 71-75.

DOI: 10.3901/jme.2007.02.071

Google Scholar

[3] Lee J Y, Nandi A K. Extraction of impacting signals using blind decomposition [J]. Journal of Sound and Vibration1999, 232(5): 945-962.

DOI: 10.1006/jsvi.1999.2778

Google Scholar

[4] Wang Hongchao, Chen Jin, Dong Guangming. Fault Diagnosis Method for Rolling Bearing's Weak Fault Based on Minimum Entropy Deconvolution and Sparse Decomposition [J]. Chinese Journal of Mechanical Engineering, 2013, 49(1)：88-94.

DOI: 10.3901/jme.2013.01.088

Google Scholar

[5] Y. Dong,M. Liao,X. Zhang, F. Wang, Fault diagnosis of rolling element bearings based on modified morphological method, Mech. Syst. Signal Process., vol. 25, no. 4, 1276–1286, May (2011).

DOI: 10.1016/j.ymssp.2010.10.008

Google Scholar

[6] N. G. Nikolaou and I. A. Antoniadis, Application of morphological operators as envelope extractors for impulsive-type periodic signals, Mech. Syst. Signal Process, vol. 17, no. 6, p.1147–1162, Nov. (2003).

DOI: 10.1006/mssp.2002.1576

Google Scholar

[7] Ho D, Randall R B. Optimization of bearing diagnostic technique using simulated and actual bearing fault signals [J]. Mechanical system and Signal Processing, 2000, 14 ( 5) : 763- 788.

DOI: 10.1006/mssp.2000.1304

Google Scholar