摘要
基于非线性频谱数据驱动方法,研究了动态系统的故障诊断问题.利用一维非线性输出频率响应函数提出一种非线性频谱特征提取方法,为了提高实时性,采用变步长自适应辨识算法进行求解;根据估计偏差实时地改变步长,兼顾了收敛速度与稳态误差;获取了非线性频谱特征之后,利用最小二乘支持向量机分类器进行故障识别.通过对提升设备的故障诊断问题进行实验研究,所得结果表明,所提出的算法识别率高,能满足在线诊断要求.
The problem of fault diagnosis for the dynamic system is studied based on the data driven method of nonlinear spectrum. An extraction method of nonlinear frequency spectrum feature is proposed by using one dimensional nonlinear output frequency response function. In order to improve timeliness, the variable step size adaptive identification algorithm is used to solve the nonlinear output frequency response function. The step size is changed according to estimating error so that convergence rate and steady state error are both considered. After obtained nonlinear frequency spectrum feature, the least square support vector machine classifier is used to fault identification. The fault diagnosis of hoisting equipment is researched, and experiments show that the proposed algorithm has the good high recognition rate that can fulfill the demand of online diagnosis.
出处
《控制与决策》
EI
CSCD
北大核心
2014年第1期168-171,共4页
Control and Decision
基金
陕西省科技项目(2010K08-13)
关键词
故障诊断
非线性频谱
自适应辨识
支持向量机
fault diagnosis
nonlinear spectrum
adaptive identification
support vector machine
