TY - GEN
T1 - Machinery condition prediction based on wavelet and support vector machine
AU - Li, Chao
AU - Liu, Shujie
AU - Zhang, Hongchao
AU - Hu, Yawei
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - This paper studies the use of wavelet and support vector machine (SVM) in machinery condition prediction. SVM is based on the VC dimension theory of statistical learning and the principle of structural risk minimization, and has shown advantages in solving the problem with limited sample, nonlinear and high dimensional pattern recognition. The soft failure of mechanical equipment makes its performance drop gradually, which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The paper models the vibration signal from the rear pad of a gas blower and analyzes the 1-step and multi-step forecasting of wavelet transformation and SVM (WT-SVM model) and SVM model.
AB - This paper studies the use of wavelet and support vector machine (SVM) in machinery condition prediction. SVM is based on the VC dimension theory of statistical learning and the principle of structural risk minimization, and has shown advantages in solving the problem with limited sample, nonlinear and high dimensional pattern recognition. The soft failure of mechanical equipment makes its performance drop gradually, which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The paper models the vibration signal from the rear pad of a gas blower and analyzes the 1-step and multi-step forecasting of wavelet transformation and SVM (WT-SVM model) and SVM model.
KW - multi-step forecasting
KW - support vector machine
KW - vibration intensity
KW - wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=84890039326&partnerID=8YFLogxK
U2 - 10.1109/QR2MSE.2013.6625909
DO - 10.1109/QR2MSE.2013.6625909
M3 - Conference contribution
AN - SCOPUS:84890039326
SN - 9781479910144
T3 - QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
SP - 1725
EP - 1729
BT - QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
Y2 - 15 July 2013 through 18 July 2013
ER -