摘要
通过精密离心机电机驱动系统和机械系统的故障机理的分析,提出了两种故障信号基于小波包分析的特征提取方法。一个是渐进性故障信号的特征提取方法,控制器误差信号通过小波包分解与重构,最后在最低频段的节点得到了已经去噪的故障信号。另一个是振动信号频带能量的特征向量提取方法,动平衡系统的振动信号被分解到独立的频段,不同频带内的信号能量变化反映了系统机械运行状态的改变,每个能量成分被提取形成特征向量用于故障诊断。试验与仿真结果表明这种基于小波包分析的故障方法具有算法简单、可行的优点。
Though the analysis of motor drive system and mechanical system failure mechanism, two type fault signal＇s character extraction method based on the algorithm of wavelet packets is presented. One is progressive fault signal, after the wavelet packet decomposition and reconfiguration, controller＇s error signal which has been well denoised can be obtained on the node of minimum frequency band. Another is eigenvector extraction method of vibration signals frequency band energy. The vibration signal of dynamic balance system is decomposed into the individual frequency bands, the variations of the signal energy in these bands reflect the different mechanical conditions, each energy ingredient is extracted to form the eigenvector for fault diagnosis. Test and simulation results demonstrate that the method of fault diagnosis using wavelet packet analysis is simple and feasible.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2005年第22期158-162,共5页
Proceedings of the CSEE
关键词
精密离心机
小波包
故障诊断
误差信号
特征向量
频带
Precision centrifuge
Wavelet packet
Fault diagnosis
Error signal
Eigenvector
Frequency band
