Angle-Based Multiblock Independent Component Analysis Method with a New Block Dissimilarity Statistic for Non-Gaussian Process Monitoring

Abstract

In recent years, the multiblock method has attracted substantial attention. Conventional multiblock methods divide an entire data set into several blocks, and the monitoring in each block is conducted separately. The multiblock method highlights local information but ignores the information among different blocks. In this paper, we propose an angle-based multiblock independent component analysis (MBICA) method and create a new block dissimilarity (BD) statistic to measure the changes between blocks. Hierarchical clustering is adopted to cluster variables with small angles into a block. ICA models are then built into each block. Support vector data description (SVDD) is introduced to yield a final monitoring decision. The changes of blocks are determined by the differences between the angles of the monitored data and the benchmark data, leading to BD statistics. The proposed MBICA-BD method is applied to the Tennessee Eastman process. The simulation results demonstrate the superiority of the MBICA-BD method