摘要
为了提高机械设备故障诊断的精度,将小波包分析与最小二乘支持向量机进行了有机的结合。首先对故障信号功率谱进行小波分解,简化了故障特征向量的提取。然后提出了一种基于最小二乘支持向量机的故障诊断模型,用二次损失函数取代支持向量机中的不敏感损失函数,将不等式约束条件变为等式约束,从而将二次规划问题转变为线性方程组的求解,用最小二乘法实现了支持向量机算法,并提出对核函数的σ参数进行动态选取,提高了诊断的准确率。仿真结果表明该模型具有较强的非线性处理和抗干扰能力。
In order to enhance fault diagnosis precision, the wavelet packet analysis and least squares support vector machine （LSSVM） are combined effectively. First the power spectrum of fault signals is decomposed by wavelet analysis, which predigests choosing method of fault eigenvectors. And then a fault diagnosis model based on LSSVM is presented. In the model, the non-sensitive loss function is replaced by quadratic loss function and the inequality constraints are replaced by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. It is presented to choose a parameter of kernel function on dynamic, which enhances preciseness rate of diagnosis. The simulation results show the model has strong non-linear solution and anti-jamming ability.
出处
《计算机科学》
CSCD
北大核心
2007年第1期289-291,共3页
Computer Science
基金
中国博士后科学基金资助项目(编号2005038515)
关键词
故障诊断
最小二乘支持向量机
核函数
小波包分析
Fault diagnosis, Least squares support vector machine,Kernel function, Wavelet packet analysis
