摘要
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。
Typical noise signals of aluminum reduction cells were collected to extract the in-depth features thus to determine the operating conditions exactly. The wavelet method was employed to denoise in preprocessing,and the Burg method and a wavelet packet were used to estimate power spectral density and extract power characteristic vectors respectively,and the two methods were compared. The results show that the Burg method is simpler and has definite physical meaning,which performs well in extracting in-depth features of stationary noise signals,while the wavelet packet method does well in extracting in-depth features of non-stationary noise signals with much transient information. A combination of the two methods can determine operating conditions of aluminum reduction cells accurately.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2009年第3期345-348,共4页
Journal of Vibration,Measurement & Diagnosis
关键词
针振
铝电解槽
频谱分析
小波包
特征提取
noise aluminum reduction cell spectral analysis wavelet packet feature extraction
