摘要
为了准确有效提取滚动轴承振动信号中最优的故障信息,判断出滚动轴承故障的类型,提出了一种基于正交匹配追踪算法和优化小波核极限学习机的滚动轴承故障诊断方法。运用正交匹配追踪算法对轴承振动信号进行降噪处理,对去噪后的信号进行小波包分解求取频带能量提取故障特征。采用基于冯诺依曼拓扑结构鲸鱼算法(Whale Optimization Algorithm,WOA)来优化WKELM的惩罚因子和核函数的参数,构造滚动轴承故障分类器模型。实验结果表明,该方法能有效提取滚动轴承故障特征信息,具有较高的诊断精度。
In order to recognize rolling bearing＇s fault types accurately according to the optimal characteristics of fault vibration signal of rolling bearing, a rolling bearing fault diagnosis method was proposed based on orthogonal matching pursuit algorithm and the optimized wavelet kernel extreme learning machine method. The OMP algorithm was used to de-noising the vibration signal of the bearing. The wavelet packet decomposition of the signal after de-noising was used to obtain the frequency band energy, and the fault characteristics were extracted. By using an improved whale optimization algorithm based on von-neumann, the penalty factor and kernel parameter of wavelet kernel extreme learning machine were optimized to design a classifier of rolling bearing＇s fault types. The experimental results prove that the proposed method can accurately and effectively identify the fault type.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2017年第9期2189-2197,共9页
Journal of System Simulation
基金
国家自然科学基金(61572238)
江苏省杰出青年基金(BK20160001)
关键词
正交匹配追踪
小波核极限学习机
鲸鱼算法
滚动轴承
orthogonal matching pursuit
wavelet kernel extreme learning machine
WOA
rolling bearing
