一种鲁棒半监督建模方法及其在化工过程故障检测中的应用

复杂化工过程的观测样本往往包含着测量噪声与少量的离群点数据，而这些受污染的数据会影响数据驱动的过程建模与故障检测方法的准确性。本文考虑了化工过程测量样本的这一实际情况，提出了一种鲁棒半监督PLVR模型（RSSPLVR），并利用核方法将其扩展为非线性的形式（K-RSSPLVR）。此类算法利用基于样本相似度的加权系数作为概率模型的先验参数，能有效消除离群点对建模的影响。利用加权后的建模样本，本文通过EM算法训练了RSSPLVR和K-RSSPLVR的模型参数，并提出了相应的故障检测算法。最后，通过TE过程仿真实验验证了所提出方法的有效性。

Robust semi-supervised modelling method and its application to fault detection in chemical processes

In most complex chemical processes, measurements are often collected with noises and some outliers. These contaminated data would have negative effect on the accuracy of data-based process modelling and fault detection. A new robust semi-supervised PLVR model (RSSPLVR) was proposed by consideration of the real measuring environment in chemical processes and extended to a nonlinear model K-RSSPLVR with a kernel methodology. In both RSSPLVR and K-RSSPLVR, a weighted coefficient based on sample similarity among all observations was used as prior checking parameter of probability model to effectively eliminate influence of outliers on modelling. Model parameter training was accomplished by analysis of the weighted dataset with EM algorithm and a fault detection scheme was developed. Finally, TE process simulation demonstrated effectiveness of the proposed modelling methods.