Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics
Traditional onefold data-driven methods for fault detection in complex process industrial systems with high-dimensional, linear, nonlinear, Gaussian, and non-Gaussian coexistence often have less than satisfactory monitoring performance because only a single distribution of process variables is consi...
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| Main Authors: | , , , , |
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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/10.1155/2022/3054860 |
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| _version_ | 1832549266118148096 |
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| author | Chenxing Xu Jiarula Yasenjiang Pengfei Cui Shengpeng Zhang Xin Zhang |
| author_facet | Chenxing Xu Jiarula Yasenjiang Pengfei Cui Shengpeng Zhang Xin Zhang |
| author_sort | Chenxing Xu |
| collection | DOAJ |
| description | Traditional onefold data-driven methods for fault detection in complex process industrial systems with high-dimensional, linear, nonlinear, Gaussian, and non-Gaussian coexistence often have less than satisfactory monitoring performance because only a single distribution of process variables is considered. To address this problem, a hybrid fault detection model based on PCA-KPCA-ICA-KICA-BI (Bayesian inference) is proposed, taking into account the advantages of principal component analysis (PCA), kernel principal component analysis (KPCA), independent component analysis (ICA), and kernel independent component analysis (KICA) in terms of dimensionality reduction and feature extraction. Foremost, this paper proposed a nonlinear evaluation method and divided the feature variables into Gaussian linear blocks, Gaussian nonlinear blocks, non-Gaussian linear blocks, and non-Gaussian nonlinear blocks by using the Jarque–Bera (JB) test and nonlinear discrimination method. Each division was monitored by the PCA-KPCA-ICA-KICA model, and finally the Bayesian fusion strategy proposed in this study is used to synthesize the detection results for each block. The hybrid model helps in evaluating variable features and bettering detection performance. Ultimately, the superiority of this hybrid model was verified through the Tennessee Eastman (TE) process and the Continuous Stirred Tank Reactor (CSTR) process, and the fault monitoring results showed an average accuracy of 85.91% for this hybrid model. |
| format | Article |
| id | doaj-art-d2de76d60c9a4517bcd13932edd704f4 |
| 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-d2de76d60c9a4517bcd13932edd704f42025-02-03T06:11:51ZengWileyInternational Journal of Chemical Engineering1687-80782022-01-01202210.1155/2022/3054860Comprehensive Monitoring of Complex Industrial Processes with Multiple CharacteristicsChenxing Xu0Jiarula Yasenjiang1Pengfei Cui2Shengpeng Zhang3Xin Zhang4College of Mechanical EngineeringCollege of Mechanical EngineeringCollege of Mechanical EngineeringChina National Petroleum CorporationChina National Petroleum CorporationTraditional onefold data-driven methods for fault detection in complex process industrial systems with high-dimensional, linear, nonlinear, Gaussian, and non-Gaussian coexistence often have less than satisfactory monitoring performance because only a single distribution of process variables is considered. To address this problem, a hybrid fault detection model based on PCA-KPCA-ICA-KICA-BI (Bayesian inference) is proposed, taking into account the advantages of principal component analysis (PCA), kernel principal component analysis (KPCA), independent component analysis (ICA), and kernel independent component analysis (KICA) in terms of dimensionality reduction and feature extraction. Foremost, this paper proposed a nonlinear evaluation method and divided the feature variables into Gaussian linear blocks, Gaussian nonlinear blocks, non-Gaussian linear blocks, and non-Gaussian nonlinear blocks by using the Jarque–Bera (JB) test and nonlinear discrimination method. Each division was monitored by the PCA-KPCA-ICA-KICA model, and finally the Bayesian fusion strategy proposed in this study is used to synthesize the detection results for each block. The hybrid model helps in evaluating variable features and bettering detection performance. Ultimately, the superiority of this hybrid model was verified through the Tennessee Eastman (TE) process and the Continuous Stirred Tank Reactor (CSTR) process, and the fault monitoring results showed an average accuracy of 85.91% for this hybrid model.http://dx.doi.org/10.1155/2022/3054860 |
| spellingShingle | Chenxing Xu Jiarula Yasenjiang Pengfei Cui Shengpeng Zhang Xin Zhang Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics International Journal of Chemical Engineering |
| title | Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics |
| title_full | Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics |
| title_fullStr | Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics |
| title_full_unstemmed | Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics |
| title_short | Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics |
| title_sort | comprehensive monitoring of complex industrial processes with multiple characteristics |
| url | http://dx.doi.org/10.1155/2022/3054860 |
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