Abstract:

Measured data abnormality in multimode batch processes directly influences model accuracy of data-driven multivariate statistical analysis and decreases performance of process monitoring and controlling. A dynamic hypersphere structure change (DHSC) derived method was proposed for detecting such data abnormality. First, mode dicing according to membership change was achieved by introducing sequence-constrained fuzzy C-means (SCFCM). Then, for each mode, support vector data description (SVDD) was used to build a static hypersphere for training data and a dynamic hypersphere for testing data. Finally, important support vectors were chosen as structures of hyperspheres and detection of abnormal data was achieved by identifying structure change of hyperspheres. Simulation experiment of penicillin fermentation process shows that the present method can achieve mode division of multimode batch processes and reduce influence of mode switch on detection accuracy of abnormal data. Using structure change of hypersphere to detect abnormal data can decrease false detection rate with high detection accuracy.

Key words: batch processes, support vector data description, multimode, abnormal data detection

摘要：

多模态间歇过程测量数据异常直接影响数据驱动的多元统计分析过程建模的准确性，导致间歇过程的监控性能降低。针对多模态间歇过程测量数据异常问题，提出了一种基于动态超球结构变化（DHSC）的多模态间歇过程测量数据异常检测方法。该方法通过引入时序约束的模糊C均值聚类（SCFCM），利用隶属度变化划分多模态间歇过程的模态；针对不同模态，采用支持向量数据描述（SVDD）建立基于训练数据的静态超球体和基于待检数据的动态超球体，选择重要的支持向量作为球体结构，进而通过识别超球体发生结构变化实现过程测量数据异常检测。青霉素发酵过程仿真实验表明，所提出的方法能够实现多模态间歇过程的模态划分，减少了模态切换对过程测量数据异常检测精度的影响，并能够根据超球体结构变化检测过程测量数据异常，具有较高的检测精度，降低了误检率。

关键词: 间歇过程, 支持向量数据描述, 多模态, 测量数据异常检测