Complex chemical processes often have multiple operating modes and the within-mode process data do not follow Gaussian or non-Gaussian distributions. To handle the problem of multiple operating modes and complex data distribution

a novel fault detection method

local standardized neighborhood preserving embedding (LSNPE) was proposed by applying local standardization (LS) strategy to the neighborhood preserving embedding (NPE) algorithm. Firstly

LSNPE algorithm was performed for dimensionality reduction and thus the main features of the collected data were extracted. At the same time

it could keep the neighborhood structure unchanged. Next

a monitoring statistics was established using the local outlier factor (LOF) of each sample in feature space and its control limit was determined. Instead of building multiple monitoring models for complex chemical process with different operating modes

the proposed LSNPE method built only one global model to monitor a multi-mode process without the support of any prior process knowledge. Finally

the feasibility and efficiency of the proposed method were illustrated through a numerical example and the Tennessee Eastman process.