A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information
Large-scale process monitoring has become a challenging issue due to the integration of sub-systems or subprocesses, leading to numerous variables with complex relationship and potential missing information in modern industrial processes. To avoid this, a distributed expectation maximization-princip...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719885499 |
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| _version_ | 1832547796391034880 |
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| author | Xuanyue Wang Xu Yang Jian Huang Xianzhong Chen |
| author_facet | Xuanyue Wang Xu Yang Jian Huang Xianzhong Chen |
| author_sort | Xuanyue Wang |
| collection | DOAJ |
| description | Large-scale process monitoring has become a challenging issue due to the integration of sub-systems or subprocesses, leading to numerous variables with complex relationship and potential missing information in modern industrial processes. To avoid this, a distributed expectation maximization-principal component analysis scheme is proposed in this paper, where the process variables are first divided into several sub-blocks using two-layer process decomposition method, based on knowledge and generalized Dice’s coefficient. Then, the missing information of variables is estimated by expectation maximization algorithm in the principal component analysis framework, then the expectation maximization-principal component analysis method is applied for fault detection to each sub-block. Finally, the process monitoring and fault detection results are fused by Bayesian inference technique. Case studies on the Tennessee Eastman process is applied to show the effectiveness and performance of our proposed approach. |
| format | Article |
| id | doaj-art-a1d8b8817a1549259dd0991f280277b0 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-a1d8b8817a1549259dd0991f280277b02025-02-03T06:43:15ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-11-011510.1177/1550147719885499A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete informationXuanyue WangXu YangJian HuangXianzhong ChenLarge-scale process monitoring has become a challenging issue due to the integration of sub-systems or subprocesses, leading to numerous variables with complex relationship and potential missing information in modern industrial processes. To avoid this, a distributed expectation maximization-principal component analysis scheme is proposed in this paper, where the process variables are first divided into several sub-blocks using two-layer process decomposition method, based on knowledge and generalized Dice’s coefficient. Then, the missing information of variables is estimated by expectation maximization algorithm in the principal component analysis framework, then the expectation maximization-principal component analysis method is applied for fault detection to each sub-block. Finally, the process monitoring and fault detection results are fused by Bayesian inference technique. Case studies on the Tennessee Eastman process is applied to show the effectiveness and performance of our proposed approach.https://doi.org/10.1177/1550147719885499 |
| spellingShingle | Xuanyue Wang Xu Yang Jian Huang Xianzhong Chen A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information International Journal of Distributed Sensor Networks |
| title | A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information |
| title_full | A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information |
| title_fullStr | A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information |
| title_full_unstemmed | A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information |
| title_short | A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information |
| title_sort | distributed expectation maximization principal component analysis monitoring scheme for the large scale industrial process with incomplete information |
| url | https://doi.org/10.1177/1550147719885499 |
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