Process monitoring approach using fast moving window PCA
Process monitoring approach using fast moving window PCA
复制标题
DOI：
10.1021/ie048873f
复制
发表时间：
2005-07
影响因子：
4.2
通讯作者：
Xun Wang;U. Kruger;G. Irwin
中科院分区：
文献类型：
--
作者：
Xun Wang;U. Kruger;G. Irwin
提出了一种自适应主成分模型的移动窗口主成分分析（MWPCA）快速算法。这结合了移动窗口内递归自适应的概念，以（i）自适应过程变量的均值和方差，（ii）自适应相关矩阵，以及（iii）通过重新计算分解来调整PCA模型。本文表明，新算法是计算速度比传统的移动窗口技术，如果窗口大小超过3倍的变量的数量，并不受窗口大小。另一个贡献是引入了N步提前地平线的过程监测。这意味着，PCA模型，识别N-步骤之前，用于分析当前的观察。对于监测复杂的化学系统，这项工作表明，地平线的使用提高了检测缓慢发展的漂移的能力。
This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.