For fault detection in dynamic processes

a novel dimensionality reduction method was proposed on the basis of improved neighbor selection in neighborhood preserving embedding algorithm

i.e.

time constrained neighborhood preserving embedding(TCNPE). Compared to neighborhood preserving embedding(NPE)

which selected neighborhood only by Euclidean distance

TCNPE selected neighborhoods within certain timeframe by k-nearest neighboring method with a consideration of time series correlation between data points and constructed a localized constraining relationship between near and distant neighborhoods within this time window. First

TCNPE algorithm extracted main features of the process data and performed linear dimensionality reduction. Next

Hotelling's T2 and SPE statistics were established for online process monitoring and kernel density estimation(KDE) was used to determine control limits. Case study and simulation of Tennessee-Eastman Process demonstrated efficacy of the proposed method.