Process monitoring based on distributed principal component analysis with angle-relevant variable selection
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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719857583 |
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| _version_ | 1849401675046977536 |
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| author | Chen Xu Fei Liu |
| author_facet | Chen Xu Fei Liu |
| author_sort | Chen Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-878a0994dc8a4e31a6f849821f15f78b |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-878a0994dc8a4e31a6f849821f15f78b2025-08-20T03:37:43ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-06-011510.1177/1550147719857583Process monitoring based on distributed principal component analysis with angle-relevant variable selectionChen XuFei LiuMultivariate 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.https://doi.org/10.1177/1550147719857583 |
| spellingShingle | Chen Xu Fei Liu Process monitoring based on distributed principal component analysis with angle-relevant variable selection International Journal of Distributed Sensor Networks |
| title | Process monitoring based on distributed principal component analysis with angle-relevant variable selection |
| title_full | Process monitoring based on distributed principal component analysis with angle-relevant variable selection |
| title_fullStr | Process monitoring based on distributed principal component analysis with angle-relevant variable selection |
| title_full_unstemmed | Process monitoring based on distributed principal component analysis with angle-relevant variable selection |
| title_short | Process monitoring based on distributed principal component analysis with angle-relevant variable selection |
| title_sort | process monitoring based on distributed principal component analysis with angle relevant variable selection |
| url | https://doi.org/10.1177/1550147719857583 |
| work_keys_str_mv | AT chenxu processmonitoringbasedondistributedprincipalcomponentanalysiswithanglerelevantvariableselection AT feiliu processmonitoringbasedondistributedprincipalcomponentanalysiswithanglerelevantvariableselection |