摘要
支持向量机是以统计学习理论为基础发展起来的新的通用学习方法,较好地解决了小样本、高维数、非线性等学习问题。本文提出了一种基于多级支持向量机分类器的滚动轴承工作状态识别方法。该方法通过时域特征参数对原信号进行特征提取,不仅计算简单,而且不考虑滚动轴承的型号和转速。试验表明这种方法具有很好的分类能力。
Support Vector Machine （SVMs） is a novel machine learning method based on statistical learning theory （SLT）. SVM is powerful for the problem with small sample, nonlinear and high dimension. A multilayer SVM classifier is applied hero to fault diagnosis of ball bearing. This method gets the preference from time zone. It is simple for calculating. Furthermore, you need not care for the class and speed of the ball bearing. It has been tested that it can have good classification ability.
出处
《计算机与现代化》
2007年第3期25-27,30,共4页
Computer and Modernization
关键词
支持向量机
多类问题
滚动轴承
故障诊断
support vector machines
multi-class problem
ball bearing
fault diagnosis
