小波阈值去噪在烟气轮机信号分析中的应用

APPLICATION OF WAVELET THRESHOLD DE-NOISING IN FLUE GAS TURBINE SIGNAL ANALYSIS

由于烟气轮机振动信号中含有大量的噪声成分,常使其非线性特征量的提取不准确,因此将基于阈值的小波去噪应用于烟气轮机振动信号分析中。首先介绍了小波阈值去噪的基本原理、阈值和阈值函数的选择方法,并对阈值函数进行了改进;然后分别对含噪Lorenz信号和实测振动信号进行小波阈值去噪实验,计算了其去噪前、后的关联维数。结果表明,小波分解后,不同尺度上信号和噪声的小波系数的分布规律明显不同,通过对其分析可以合理选择小波分解的尺度;在此基础上对小波系数进行阈值处理并重构,能有效地去除信号中含有的噪声,很好地保存信号的局部特征;去噪后信号的伪相图更加规则,关联维数估计值更加合理。该方法能提高信号分析的准确率。

Because of the influence of the noise contained in vibration signal,the nonlinear characteristic parameters of flue gas turbine are usually extracted incorrectly.In order to eliminate the noise,the wavelet threshold de-noising is applied in this paper.Firstly,the algorithm of this method is introduced.It is based on the theory of wavelet transform.The threshold and threshold function are selected and improved.By wavelet decomposition,the wavelet coefficients of the original signal and the noise on different scales are obviously different.According to this,the scale of decomposition can be chosen properly.Secondly,this method is tested by the signal of Lorenz attractor and the flue gas turbine signal.The result shows that this method can reduce the noise effectively and the local feature of the original signal is well reserved.Compared with the pseudo-phase portrait reconstructed from signal contained noise,the pseudo-phase portrait reconstructed after the wavelet threshold de-noising is more regular.By this method,the correlation dimension,which can reflect different fault condition for nonlinear system,is estimated accurately.It is proved that this method can improve the accuracy of signal analysis.