摘要
针对车辆传动箱振动信号的非线性,提出一种将多重分形与支持向量机相结合的状态识别方法。运用奇异谱和广义维数来描述其振动信号特征,并将其作为支持向量机的输入特征量。将改进的混沌粒子群算法引入到支持向量机参数优化中,实现对惩罚函数c和径向基函数σ的智能优化选取。实验结果表明,该方法建立的SVM分类模型能够对车辆传动箱不同运行状态进行分类,并且具有更高的准确率。
For the nonlinear vibration signals caused by vehicle gearbox,a state recognition method combined of multi-fractal and support vector machine is proposed.The vibration signal characteristics are described by using the singular spectrum and generalized dimension which as the input into characteristics of SVM.The intelligent optimization selected of penalty function c and radial basis function σ is realized are through introducing the improved chaotic particle swarm optimization algorithm into support vector machine parameter optimization.The results show that the different operational status of the vehicle gearbox can be classified by using SVM classification model established by this method,and the accuracy is increased.
出处
《机械传动》
CSCD
北大核心
2015年第5期165-168,共4页
Journal of Mechanical Transmission
关键词
多重分形
混沌粒子群
支持向量机
车辆传动箱
状态识别
Multi--fractal
Chaos particle swam optimization
Support vector machine
Vehicle gearbox
State recognition
