Abstract
Multivariate statistics process monitoring can achieve dimensionality reduction and latent feature extraction on process variables. However, process variables without beneficial information may affect the monitoring performance. This article proposes a distributed principal component analysis method based on the angle-relevant variable selection for plant-wide process monitoring. The directions of principal components are utilized to construct the sub-blocks, where the variables in each sub-block are determined by angle. After establishing the principal component analysis model in each sub-block, the monitoring results are fused by Bayesian inference. The simulation results show that the proposed method can select the responsible variables effectively and enhance the monitoring performance.
Highlights
The monitoring and diagnosis of a chemical process is crucially important to ensure the safety and the quality of the product.[1,2,3,4,5,6,7] With the rapid development of the modern industries, a large amount of data emerges and a new challenge to multivariate statistics process monitoring (MSPM) is given
In ABPCA, the block divisions are implemented through an automatic way in principal components (PC) directions and a variable selection method based on the angle is used in each sub-block.[35,36]
A distributed PCA (DPCA) method named as ABPCA method is proposed, where the variables are selected by angle in each sub-block
Summary
The monitoring and diagnosis of a chemical process is crucially important to ensure the safety and the quality of the product.[1,2,3,4,5,6,7] With the rapid development of the modern industries, a large amount of data emerges and a new challenge to multivariate statistics process monitoring (MSPM) is given. In the area of MSPM, some approaches have been reported.[8,9,10,11,12] Among these methods, principal component analysis (PCA) is the most basic and widely used method.[13,14] By projecting the data into two low-dimensional spaces (the principal component space (PCS) and the residual space (RS)), the high-dimensional and correlative data can be effectively operated. The traditional PCA methods cannot serve well for complex plant-wide process monitoring.[15,16]
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