@inproceedings{37da1dbd57124742bc8d4e92a822a66b,
title = "Machinery condition prediction based on wavelet and support vector machine",
abstract = "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.",
keywords = "multi-step forecasting, support vector machine, vibration intensity, wavelet transform",
author = "Chao Li and Shujie Liu and Hongchao Zhang and Yawei Hu",
year = "2013",
doi = "10.1109/QR2MSE.2013.6625909",
language = "English",
isbn = "9781479910144",
series = "QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering",
pages = "1725--1729",
booktitle = "QR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering",
note = "null ; Conference date: 15-07-2013 Through 18-07-2013",
}