基于自组织神经网络与核密度估计的非线性MPCA批过程监控（英文）

Monitoring of batch process based on autoassociative neural network and kernel density estimation nonlinear MPCA

针对多向主元分析(MPCA)不能提取复杂的非线性系统变量间的非线性特性以及T2统计量置信限的确定是以主元得分呈正态分布为假设前提的情况,提出了一种基于自组织神经网络与核密度估计的非线性MPCA在线故障监测方法。该方法用自组织神经网络去提取变量间的非线性特征信息;用核概率密度函数去估计非线性主元的置信限。将该方法应用到链霉菌补料分批发酵过程的在线故障监测中,应用效果表明用非线性主元比用同样数目的线性主元能够获取更多的变量信息,并且用核密度估计置信限的方法比用参数估计的方法能更准确地对故障进行监测。

Multiway principal components analysis （MPCA） is a linear model in nature, thus, limited when it is applied to batch process. In this paper, the linear model MPCA was complemented with an autoassociative neural network model in order to generate nonlinear principal components. The network＇s bottleneck layer outputs （nonlinear principal components） were made orthogonal. A method to estimate confidence limits based on a kernel probability density function was proposed since the nonlinear scores are no normally distributed. A statistic-like parameter （DNL） was proposed to evaluate on-line scores for new runs using the density estimated confidence bounds and replacing the T^2 statistic. The proposed method was applied to monitoring fed-batch streptomycete production, and the simulation results show that the nonlinear scores obtained with the autoassociative neural networks capture more process data variance than if obtained with a linear method and the density estimation method proved to be more reliable.