Independent component analysis (ICA) method is mainly used to monitor linear and non-Gaussian process

a method named modified independent analysis (MICA) is proposed to improve the non-Gaussian process monitoring performance. The method uses the information of process data to modify the monitoring process monitoring performance. The method uses the information of process data to modify the monitoring index of ICA. Many industrial process variables have characteristics of nonlinear

non-Gaussian and Gaussian mixture distribution. A method which based on LTSA algorithm and the combined index is proposed to solve these problems of the industrial process. Firstly

the local tangent space alignment (LTSA) algorithm is used to achieve the nonlinear dimensionality reduction of sample data. Then MICA and PCA methods are used to obtain non-Gaussian and Gaussian statistics

and the new statistic

which is weighted by these two statistics

is used for process monitoring. Finally

the proposed method has been applied to monitor the Tennessee-Eastman(TE) process and the ethylene cracking furnace to show its efficiency.