关键词:

集成学习, 随机子空间方法, 主元分析, 故障检测

Abstract:

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.

Key words:

集成学习, 随机子空间方法, 主元分析, 故障检测