# An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

arXiv: https://arxiv.org/abs/2012.06830 · HTML: https://mlsanity.com/paper/2012.06830/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Second-Order Component Analysis for Fault Detection](https://mlsanity.com/paper/2103.07303/) (2021)
- [Probabilistic Geometric Principal Component Analysis with application to neural data](https://mlsanity.com/paper/2509.18469/) (2025)
- [A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems](https://mlsanity.com/paper/2512.05971/) (2025)
- [Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation](https://mlsanity.com/paper/2101.08579/) (2021)
- [Self-learning sparse PCA for multimode process monitoring](https://mlsanity.com/paper/2108.03449/) (2021)
- [Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring](https://mlsanity.com/paper/2202.11295/) (2022)

## Abstract (arXiv)

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.