【摘要】 根据Bently实验台所采集的碰摩、松动、不对中、不平衡4种典型汽轮机转子振动故障信号,采用小波包分析和Lyapunov指数相结合的方法对其进行故障诊断。先对原始信号采用小波包分析进行滤波,提取有用的信号频段,再对滤波后的信号进行二次处理,提取混沌特征量——Lyapunov指数进行故障定位。诊断结果表明:小波包分析的方法有着很好的滤波和提取非平稳信号的能力;小波包分解重构后的汽轮机转子的振动时间序列在不同故障状态下的Lyapunov指数明显不同,因此,Lyapunov指数在进行汽轮机转子故障类型诊断时有较好的区分度。更多还原

【Abstract】 According to the four typical fault signals of turbine rotor vibration,including rubbing,loosening,misalignment and mass unbalance which are collected from the Bently experiment set,the method which combines wavelet packet analysis and Lyapunov exponent is adopted to diagnose the faults.First,wavelet packet analysis is used for filtering the fault signals,extracting useful signal frequency segments from original signals.Then the filtered signals are treated again to extract the chaos characteristic quantity,i.e.Lyapunov exponent for fault location.The diagnostic results indicate that wavelet packet analysis is of good ability of filtering and extracting nonstationary signal;the Lyapunov exponent of vibration time series can distinguish different fault conditions,and is of good discriminability in turbine rotor fault diagnosis.更多还原