论文标题
非线性数据驱动过程监视的概率PCA的改进混合物的改进
An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
论文作者
论文摘要
在本文中，引入了概率主成分分析（PPCA）的改进混合物（PPCA）。为了实现这一目的，利用概率主要成分分析仪混合的技术来建立与本地PPCA模型的基础非线性过程的模型，其中基于在基于修改的PPCA基于修改的PPCA的故障检测方法中的两个监视统计量的整合提出了一种新型的复合监测统计量。此外，上述监测统计数据的加权平均值被用作指标来检测潜在的异常。与几种无监督算法相比，已经讨论了所提出的算法的优点。最后，采用了田纳西州伊士曼进程和自动悬浮模型，以进一步证明拟议计划的有效性。
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.
