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

TL;DR

This paper introduces OLPP-MLE, a novel process monitoring method combining orthogonal locality preserving projection and maximum likelihood estimation to improve fault detection accuracy and robustness.

Contribution

The paper presents a new data-driven process monitoring approach integrating OLPP for dimensionality reduction and MLE for intrinsic dimensionality estimation, with novel static fault detection measures.

Findings

Effective fault detection demonstrated in three case studies.

OLPP outperforms traditional locality preserving methods.

Kernel density estimation simplifies threshold setting.

Abstract

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 $T_{\scriptscriptstyle {OLPP}}^2$ 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.