Process monitoring based on distributed principal component analysis with angle-relevant variable selection

Chen Xu and Fei Liu

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 6, 1550147719857583

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.

Keywords: Distributed monitoring; principal component analysis; angle-relevant variable selection; Bayesian inference (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:6:p:1550147719857583

DOI: 10.1177/1550147719857583

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