Study and application of method of extracting outliers in statistical monitoring model based on robust scaling
基于鲁棒尺度的统计建模数据中异常点去除算法的研究及应用

Because statistical monitoring model based on PCA is strongly affected by outlying observations. The outlier in historical data acquired from industry process can decrease ability of process performance monitoring. This paper proposed a new outlier detection combined method based on robust scaling closest distance to center (CDC) and ellipsoidal multivariate trimming (MVT) after a summary on principle and limitation of robust outlier detection method. Applied the algorithm to extract outliers from a fermentation process and compared with the CDC and MVT outlier detection algorithms. The application results show that the proposed algorithm can effectively extract the outliers from the modeling database.