# Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

arXiv: https://arxiv.org/abs/2012.07021 · HTML: https://mlsanity.com/paper/2012.07021/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection T_scriptscriptstyle OLPP2 and rm SPE_scriptscriptstyle OLPP are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies.