标题： 一种改进的非线性数据驱动过程监测的概率PCA混合方法 显示英文标题

标题： An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

摘要： 一种改进的概率主成分分析（PPCA）混合模型已被引入用于非线性数据驱动的过程监测。 为了实现这一目的，利用概率主成分分析器的混合技术来建立底层非线性过程的模型，其中基于改进的PPCA故障检测方法中的两个监测统计量的集成提出了一种新的复合监测统计量。 此外，上述监测统计量的加权均值被用作检测潜在异常的指标。 所提出的算法的优点与几种无监督算法进行了比较讨论。 最后，采用田纳西东曼过程和一个自悬浮模型来进一步证明所提出方案的有效性。 显示英文摘要

摘要： An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analysers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilised as a metrics to detect potential abnormalities. The virtues of the proposed algorithm have been discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further.