  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.
