Dozens of advantages have been reported on using support vector data description (SVDD) in the fields of non-Gaussian process monitoring and fault diagnosis.However

during the SVDD model construction offline

usually the whole training data set is used.Due to the tremendous size of the data set

the computation burden for modeling is quite heavy

which leads to the difficulty in updating model online.Therefore

this paper proposes a fast SVDD algorithm on the basis of the feature samples.In this new algorithm

the feature samples are used in stead of the whole training data set for modeling in order to significantly reduce the computation complexity.Concurrently

PCA is replaced by the local tangent space alignment (LTSA) to extract the underlying manifold structure of the process data set

since the traditional dimension reduction methods

such as PCA

have poor capability to handle nonlinearity.Next SVDD is applied on the manifold.At last

corresponding statistical indices are used for fault detection purpose.The proposed method has been tested on the Tennessee Eastman (TE) process

while the simulation results show the efficiency of it.