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

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  • Chen Xu
  • Fei Liu

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.

Suggested Citation

  • Chen Xu & Fei Liu, 2019. "Process monitoring based on distributed principal component analysis with angle-relevant variable selection," International Journal of Distributed Sensor Networks, , vol. 15(6), pages 15501477198, June.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:6:p:1550147719857583
    DOI: 10.1177/1550147719857583
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    1. Hua Song & Pengqian Han & Junxiang Zhang & Chunhua Zhang, 2018. "Fault diagnosis method for closed-loop satellite attitude control systems based on a fuzzy parity equation," International Journal of Distributed Sensor Networks, , vol. 14(10), pages 15501477188, October.
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