大型铝电解槽针振信号深层特征提取方法研究

为了提取大型铝电解槽针振信号的深层特征以便精确识别槽况，收集了典型的针振信号，利用小波除噪技术进行预处理，分别用现代谱估计算法对信号功率谱进行估计和小波包算法提取信号能量特征向量。对两种算法进行比较的结果表明，现代谱估计算法简单，物理意义明确，能很好地提取平稳性好的针振信号的深层特征，而小波包分解算法则能很好地提取平稳性差、突变信息多的针振信号的深层特征。

In-Depth Feature Extraction of Noise Signals in Aluminum Reduction Cells

Typical noise signals of aluminum reduction cells were collected to e xtract the in depth features thus to determine the operating conditions exactly . The wavelet method was employed to denoise in preprocessing, and the Burg metho d an d a wavelet packet were used to estimate power spectral density and extract powe r characteristic vectors respectively, and the two methods were compared. The re sults show that the Burg method is simpler and has definite physical meaning, wh ich performs well in extracting in depth features of stationary noise signals, w hile the wavelet packet method does well in extracting in depth features of non stationary noise signals with much transient information. A combination of the t wo methods can determine operating conditions of aluminum reduction cells accura tely.