To handle the modeling problem for high-dimension data

the independent component subspace method (ICSM) was proposed.As a new ensemble learning method

ICSM could overcome the main drawback of the random subspace method.It constructed subspaces according to independent components (ICs) contributions on different process variables.As a result

the modeling requirement of the ensemble learning method was satisfied

and its physical meaning was also well presented.Moreover

a new fault detection method named ICSM-PCA was also developed.Firstly

PCA monitoring models were build on different subspaces

then the weighted value of each model was computed based on T2 and SPE statistics.Finally，two ensemble statistics could be built for monitoring industrial processes.A case study of the Tennessee-Eastman (TE) process illustrated that the proposed method showed good modeling performance and exhibited satisfactory fault detection ability.