自适应冗余多小波及其在故障诊断中的应用

针对机械设备在运行过程中萌生的故障尚在特征不明显、特征信息微弱且往往被机械设备运行过程中的强噪声所淹没等给故障特征提取与故障定位带来了很大困难,提出了自适应冗余多小波的故障诊断方法.基于Chui-Lian多小波,依据信号特点采用两尺度相依变换方法,以谱熵最小为优化目标、遗传算法为优化方法,实现了冗余多小波的自适应构造.同时,对振动信号进行了冗余多小波分解,从而实现了对故障的准确定位及特征提取.将提出的方法应用于滚动轴承的故障分析和烟汽轮机的碰摩故障诊断中,结果显示,该方法可以有效地提高对机械设备在运行中产生故障的诊断能力.对比结果表明,该方法明显地优于传统的傅里叶变换、Db6单小波变换和原始CL3多小波变换等方法.

Adaptive Redundant Multiwavelet with Applications to Fault Diagnosis

Signals of mechanical equipment faults in operation with obscure symptoms and weak features are always contaminated by stronger background noise.To solve the difficulty,a new method called adaptive redundant multiwavelet is proposed.Following Chui-Lian multiwavelet and two-scale similarity transforms,and taking the minimum envelope spectrum entropy as the optimization objective and genetic algorithms as the optimization tool,the redundant multiwavelet is adaptively constructed.Compared with the Fourier transform,Db6 scalar wavelet transform and CL3 multiwavelet transform,the applications to fault diagnosis rub-impact for a rolling element bearing of outer-race and a flue gas turbine unit of show the improved effectiveness of the proposed method.