An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
This work addresses the problem of detecting abnormalities in nonlinear industrial processes for process engineers, offering an incremental improvement to existing PPCA-based fault detection methods.
This paper introduces an improved mixture of probabilistic PCA for nonlinear data-driven process monitoring. It proposes a novel composite monitoring statistic and uses the weighted mean of monitoring statistics to detect abnormalities, demonstrating its effectiveness on the Tennessee Eastman process and an autosuspension model.
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.