A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material
In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states. In this situation, it is difficult for the traditional single-mode monitoring methods to accurately detect the process a...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7203 |
| _version_ | 1797476967713341440 |
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| author | Ning Chen Fuhai Hu Jiayao Chen Kai Wang Chunhua Yang Weihua Gui |
| author_facet | Ning Chen Fuhai Hu Jiayao Chen Kai Wang Chunhua Yang Weihua Gui |
| author_sort | Ning Chen |
| collection | DOAJ |
| description | In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states. In this situation, it is difficult for the traditional single-mode monitoring methods to accurately detect the process abnormalities. To this end, a multimode monitoring method based on the factor dynamic autoregressive hidden variable model (FDALM) for industrial processes is proposed in this paper. First, an improved affine propagation clustering algorithm to learn the model modal factors is adopted, and the FDALM is constructed by combining multiple high-order hidden state Markov chains through the factor modeling technology. Secondly, a fusion algorithm based on Bayesian filtering, smoothing, and expectation-maximization is adopted to identify model parameters. The Lagrange multiplier formula is additionally constructed to update the factor coefficients by using the factor constraints in the solving. Moreover, the online Bayesian inference is adopted to fuse the information of different factor modes and obtain the fault posterior probability, which can improve the overall monitoring effect of the model. Finally, the proposed method is applied in the sintering process of ternary cathode material. The results show that the fault detection rate and false alarm rate of this method are improved obviously compared with the traditional methods. |
| first_indexed | 2024-03-09T21:12:19Z |
| format | Article |
| id | doaj.art-a57e96b42438476299dc1dbd8f51eaac |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:12:19Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-a57e96b42438476299dc1dbd8f51eaac2023-11-23T21:45:09ZengMDPI AGSensors1424-82202022-09-012219720310.3390/s22197203A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode MaterialNing Chen0Fuhai Hu1Jiayao Chen2Kai Wang3Chunhua Yang4Weihua Gui5School of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaIn industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states. In this situation, it is difficult for the traditional single-mode monitoring methods to accurately detect the process abnormalities. To this end, a multimode monitoring method based on the factor dynamic autoregressive hidden variable model (FDALM) for industrial processes is proposed in this paper. First, an improved affine propagation clustering algorithm to learn the model modal factors is adopted, and the FDALM is constructed by combining multiple high-order hidden state Markov chains through the factor modeling technology. Secondly, a fusion algorithm based on Bayesian filtering, smoothing, and expectation-maximization is adopted to identify model parameters. The Lagrange multiplier formula is additionally constructed to update the factor coefficients by using the factor constraints in the solving. Moreover, the online Bayesian inference is adopted to fuse the information of different factor modes and obtain the fault posterior probability, which can improve the overall monitoring effect of the model. Finally, the proposed method is applied in the sintering process of ternary cathode material. The results show that the fault detection rate and false alarm rate of this method are improved obviously compared with the traditional methods.https://www.mdpi.com/1424-8220/22/19/7203multimodalityfactor modelingprocess monitoringFDALM |
| spellingShingle | Ning Chen Fuhai Hu Jiayao Chen Kai Wang Chunhua Yang Weihua Gui A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material Sensors multimodality factor modeling process monitoring FDALM |
| title | A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material |
| title_full | A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material |
| title_fullStr | A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material |
| title_full_unstemmed | A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material |
| title_short | A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material |
| title_sort | monitoring method based on fdalm and its application in the sintering process of ternary cathode material |
| topic | multimodality factor modeling process monitoring FDALM |
| url | https://www.mdpi.com/1424-8220/22/19/7203 |
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