IEEE Trans Cybern. 2019 Jan;49(1):198-210. doi: 10.1109/TCYB.2017.2771229. Epub 2017 Dec 4.
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 analyzers 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 utilized as a metrics to detect potential abnormalities. The virtues of the proposed algorithm are 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.
本文提出了一种改进的概率主成分分析（PPCA）混合方法，用于非线性数据驱动的过程监控。为了实现这一目的，利用混合概率主成分分析器技术，基于局部 PPCA 模型建立了基础非线性过程的模型，其中基于改进的 PPCA 故障检测方法，提出了一种新的组合监测统计量。此外，上述监测统计量的加权平均值用作度量标准，以检测潜在的异常情况。将所提出的算法与几种无监督算法进行了比较，讨论了其优点。最后，使用田纳西东曼过程和自动悬浮模型进一步验证了所提出方案的有效性。
