Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation
This work provides an incremental improvement in process monitoring for industrial applications by combining existing dimensionality reduction and estimation techniques.
This paper introduces OLPP-MLE, a new data-driven method for process monitoring that integrates orthogonal locality preserving projection (OLPP) for dimensionality reduction with maximum likelihood estimation (MLE) for intrinsic dimensionality estimation. The method defines two static measures, T_OLPP^2 and SPE_OLPP, for fault detection and uses kernel density estimation to compute thresholds for fault diagnosis, demonstrating its effectiveness through three case studies.
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.