【作者】 田进；

【导师】 谷俊杰；

【作者基本信息】 华北电力大学（河北） ， 热能工程， 2008， 硕士

【摘要】 针对离心风机的运行状态判别和多类故障诊断问题,对电厂离心风机几种常见机械故障进行试验,对其产生机理进行了深入地试验研究。在此基础上,运用小波理论对其初始特征振动信号进行降噪处理和小波多分辨分解、小波包分解处理,分析故障状态表现的不同特征,进行定性的故障诊断,计算所得信号的分形维数,量化故障特征,进行定量的故障诊断,实现故障类型的判别及确定故障的敏感频带。诊断结果表明上述方法明显的优于传统的基于FFT的诊断方法,尤其对于非稳态故障信号,有很强的直观性和区分度,是一种有效的风机状态监测和故障诊断方法。更多还原

【Abstract】 In accordance with the problems of the fan condition monitoring and fault diagnosis, several kinds of common mechanical faults of the fan is tested in a large fan test-bed and the fault mechanism and its spectrum characteristic are researched. On this basis, using the wavelet theory to de-noise the initial characteristic vibration signal, using wavelet multi-resolution analysis, wavelet packet analysis to analyzing different characteristics of the fault states, make qualitative fault diagnosis;And calculating correlation dimension of the signal of incomes, quantizing characteristics of fault, make quantitative fault diagnosis, realize the discrimination of the fault type, and confirm the sensitive frequency band of the faults.Based on the above analysis, proposing wavelet correlation dimension, wavelet multi-resolution correlation dimension, wavelet packet correlation dimension,wavelet approximate entropy the methods of fault diagnose.Result shows that the above method is superior to the traditional diagnosis method based FFT,which is more visual and capable of specification.It is a kind effective method of monitoring and fault diagnosis of fan.更多还原