Modified t-Distribution Stochastic Neighbor Embedding Using Augmented Kernel Mahalanobis-Distance for Dynamic Multimode Chemical Process Monitoring
The traditional data-driven process monitoring methods may not be applicable for the system which has dynamic and multimode characteristics. In this paper, a novel scheme named modified t-distribution stochastic neighbor embedding using augmented Mahalanobis-distance for dynamic multimode chemical p...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/8460463 |
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| author | Haoyu Gu Li Wang |
| author_facet | Haoyu Gu Li Wang |
| author_sort | Haoyu Gu |
| collection | DOAJ |
| description | The traditional data-driven process monitoring methods may not be applicable for the system which has dynamic and multimode characteristics. In this paper, a novel scheme named modified t-distribution stochastic neighbor embedding using augmented Mahalanobis-distance for dynamic multimode chemical process monitoring (AKMD-t-SNE) is proposed to realize dynamic multimodal process monitoring. First, the augmented matrix strategy is utilized to ensure the sample contains the autocorrelation of the process. Moreover, AKMD-t-SNE method eliminates the scale and spatial distribution differences among multiple modes by calculating the kernel Mahalanobis distance between the samples to establish the global model. The features extracted via the proposed method are obviously non-Gaussian, and there will be a deviation in the construction of traditional statistics. Then, the support vector data description (SVDD) method is used to construct statistics to deal with this problem. In addition, a hybrid correlation coefficient method (HCC) is proposed to achieve fault isolation and improve the accuracy of isolation results. The advantages of the proposed scheme are illustrated by a numerical case and the Multimode Tennessee Eastman Process (MTEP) benchmark. |
| format | Article |
| id | doaj-art-d41756634c1147259a789e88bf02f9a1 |
| institution | Kabale University |
| issn | 1687-8078 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-d41756634c1147259a789e88bf02f9a12025-08-20T03:25:12ZengWileyInternational Journal of Chemical Engineering1687-80782022-01-01202210.1155/2022/8460463Modified t-Distribution Stochastic Neighbor Embedding Using Augmented Kernel Mahalanobis-Distance for Dynamic Multimode Chemical Process MonitoringHaoyu Gu0Li Wang1School of Electrical and Electronic EngineeringSchool of Electrical and Electronic EngineeringThe traditional data-driven process monitoring methods may not be applicable for the system which has dynamic and multimode characteristics. In this paper, a novel scheme named modified t-distribution stochastic neighbor embedding using augmented Mahalanobis-distance for dynamic multimode chemical process monitoring (AKMD-t-SNE) is proposed to realize dynamic multimodal process monitoring. First, the augmented matrix strategy is utilized to ensure the sample contains the autocorrelation of the process. Moreover, AKMD-t-SNE method eliminates the scale and spatial distribution differences among multiple modes by calculating the kernel Mahalanobis distance between the samples to establish the global model. The features extracted via the proposed method are obviously non-Gaussian, and there will be a deviation in the construction of traditional statistics. Then, the support vector data description (SVDD) method is used to construct statistics to deal with this problem. In addition, a hybrid correlation coefficient method (HCC) is proposed to achieve fault isolation and improve the accuracy of isolation results. The advantages of the proposed scheme are illustrated by a numerical case and the Multimode Tennessee Eastman Process (MTEP) benchmark.http://dx.doi.org/10.1155/2022/8460463 |
| spellingShingle | Haoyu Gu Li Wang Modified t-Distribution Stochastic Neighbor Embedding Using Augmented Kernel Mahalanobis-Distance for Dynamic Multimode Chemical Process Monitoring International Journal of Chemical Engineering |
| title | Modified t-Distribution Stochastic Neighbor Embedding Using Augmented Kernel Mahalanobis-Distance for Dynamic Multimode Chemical Process Monitoring |
| title_full | Modified t-Distribution Stochastic Neighbor Embedding Using Augmented Kernel Mahalanobis-Distance for Dynamic Multimode Chemical Process Monitoring |
| title_fullStr | Modified t-Distribution Stochastic Neighbor Embedding Using Augmented Kernel Mahalanobis-Distance for Dynamic Multimode Chemical Process Monitoring |
| title_full_unstemmed | Modified t-Distribution Stochastic Neighbor Embedding Using Augmented Kernel Mahalanobis-Distance for Dynamic Multimode Chemical Process Monitoring |
| title_short | Modified t-Distribution Stochastic Neighbor Embedding Using Augmented Kernel Mahalanobis-Distance for Dynamic Multimode Chemical Process Monitoring |
| title_sort | modified t distribution stochastic neighbor embedding using augmented kernel mahalanobis distance for dynamic multimode chemical process monitoring |
| url | http://dx.doi.org/10.1155/2022/8460463 |
| work_keys_str_mv | AT haoyugu modifiedtdistributionstochasticneighborembeddingusingaugmentedkernelmahalanobisdistancefordynamicmultimodechemicalprocessmonitoring AT liwang modifiedtdistributionstochasticneighborembeddingusingaugmentedkernelmahalanobisdistancefordynamicmultimodechemicalprocessmonitoring |