  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.
