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.

翻译：为达到这一目的,采用了一种将概率主要成分分析混合在一起的方法,以建立基础非线性主要成分分析模型和当地PPCA模型的模型,其中根据将两种监测统计数据综合纳入以PPCA为基础的经修订的缺陷检测方法,提出了新的综合监测统计数据,此外,上述监测统计数据的加权平均值被用作检测潜在异常的衡量标准,与若干未经监督的算法比较,讨论了拟议算法的优点,最后,田纳西东部过程和自动悬浮模型被用来进一步证明拟议计划的有效性。