As industrial processes become more complex

on-line monitoring of the processes are gaining importance for plant safety

maintenance

and product quality. To handle the nonlinear problem for process monitoring

a novel fault detection method was proposed by combining locally linear embedding (LLE)with support vector data description (SVDD).Firstly

LLE manifold learning algorithm was performed for nonlinear dimensionality reduction and thus the main feature of the collected data was extracted. Then

the mapping matrix from data space to feature space was calculated by using local linear regression which guaranteed the real-time property of the algorithm. Next

in order to avoid the influence of data noise on the traditional statistics

a fault detection model was established based on SVDD in the feature space

while a corresponding statistic and its control limit were determined at the same time. Finally

the feasibility and efficiency of the proposed method were shown through a numerical simulation and the Tennessee Eastman (TE)benchmark process.