基于小波包与模糊聚类算法的声波法检测流化床内颗粒的平均粒度

平均粒径是气固流化床反应器运行时需要监控的重要参数之一,利用声波信号检测床内颗粒平均粒度的方法能克服传统方法不能实时在线测量的缺陷,安全环保不侵入流场.先用Db5小波包将声发射信号3尺度分解,求出各细节信号小波系数的绝对值加和,构成声信号的能量模式,标准化之后经主成分分析得出主成分,再用模糊均值聚类方法分类.由于不同粒度的声波信号经小波包分解后,其小波系数绝对值加和具有特定的模式,因而,这种方法分类准确性达98%以上.

Average particle size measuring by acoustic signal for fluidized bed based on wavelet packet and fuzzy cluster algorithm

Average particle size is one of the key parameters need to be supervisory controlled for fluidized bed reactors.Measuring the average particle size by acoustic emission(AE)signal is superior to traditional methods because it is safety,environmental protection, non-invasive,and the average particle size can be measured at real time and online.Original AE signals received from a detector were firstly decomposed by the wavelet packet with Daubechies level five wavelet(db5).The absolute wavelet coefficients were summed and the summations were known as energy pattern,which used for classification.Principal Component Analysis(PCA)was applied to the summations after they had been normalized.Some of these principal components were classified through fuzzy cluster algorithm.Due to acoustic emission signals originated from different particle sizes are different from each other;the energy patterns originated from those AE signals are distinctly different,too.Measuring the average particle size in the fluidized bed by AE signal got over 98% of accuracy.