Title :
Cooling fan bearing diagnosis based on AR& MED
Author :
Liu, Chaoqin ; Zhou, Xue ; Yang, Shuai ; Liang, Wei ; Miao, Qiang
Author_Institution :
Sch. of Mech., Electron. & Ind. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
To monitor the initial failure of cooling fan´s rolling bearing, this paper reviews the theory of autoregressive (AR) model and the minimum entropy deconvolution (MED) filtering technique. The AR method can remove the deterministic components of the original signal, and the MED filter could reduce the effect of the transmission path. These two filtering techniques were combined in this paper to pre-process the rolling bearing´s vibration signal, and then the envelope spectrum of the residual signal was analyzed. The method leads to efficient filtering result.
Keywords :
autoregressive processes; cooling; deconvolution; entropy; failure (mechanical); fans; fault diagnosis; filtering theory; mechanical engineering computing; rolling bearings; vibrations; autoregressive model; cooling fan bearing diagnosis; deterministic component removal; envelope spectrum; failure monitoring; minimum entropy deconvolution filtering technique; residual signal; rolling bearing; transmission path; vibration signal preprocessing; Cooling; Deconvolution; Educational institutions; Entropy; Filtering; Rolling bearings; Vibrations; AR; MED; cooling fan; initial failure diagnosis;
Conference_Titel :
Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE), 2012 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4673-0786-4
DOI :
10.1109/ICQR2MSE.2012.6246310