基于SOM的高维化工过程数据粗差判别

Outlier Detection of High Dimensional Chemical Engineering Process Data Based on Self-Organizing Map

针对石油化工生产过程样本数据呈高维的特征,提出了基于自组织映射(Self-Organizing Map,SOM)网络的粗差判别方法,并实际应用于初馏塔生产过程。首先应用SOM网络对初馏塔生产过程数据进行保留拓扑结构的降维映射,然后通过对其映射平面神经元间距离的可视化分析,实现数据粗差判别。研究结果表明用SOM网络来发现高维复杂生产过程数据中的粗差具有很好的可视化效果及应用前景。

For the high dimensional chemical engineering processing data, an outlier detection method based on self organizing map （SOM）networks and its visualization methods was proposed. Practically, it was applied for the observed data of preflash tower and the satisfactory result was obtained. Firstly, SOM was applied to obtain the topology preserving plane for the high dimensional data. Then, based on the mapping plane and its visualization methods, the outliers were visualized clearly and easily. The results show that the proposed method does not need complex calculation, and the outliers in high dimensional data are effectively detected and eliminated.