Digital Twin-Based Fault Diagnosis Platform for Final Rolling Temperature in Hot Strip Production
The final rolling temperature in hot rolling is an important process parameter for hot-rolled strips and greatly influences their mechanical properties and rolling stability. The diagnosis of final rolling temperature anomalies in hot rolling has always been difficult in industry. A data-driven risk...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/16/21/7021 |
| _version_ | 1797631640934023168 |
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| author | Chen Desheng Shao Jian Li Mingxin Xiang Sensen |
| author_facet | Chen Desheng Shao Jian Li Mingxin Xiang Sensen |
| author_sort | Chen Desheng |
| collection | DOAJ |
| description | The final rolling temperature in hot rolling is an important process parameter for hot-rolled strips and greatly influences their mechanical properties and rolling stability. The diagnosis of final rolling temperature anomalies in hot rolling has always been difficult in industry. A data-driven risk assessment method for detecting final rolling temperature anomalies is proposed. In view of the abnormal setting value for the strip head, a random forest model is established to screen the process parameters with high feature importance, and the isolation forest algorithm is used to evaluate the risk associated with the remaining parameters. In view of the abnormal process curve of the full length of the strip, the Hausdorff distance algorithm is used to eliminate samples with large deviations, and a risk assessment of the curve is carried out using the <i>LCSS</i> algorithm. Aiming to understand the complex coupling relationship between the influencing factors, a method for identifying the causes of anomalies, combining a knowledge graph and a Bayesian network, is established. According to the results of the strip head and the full-length risk assessment model, the occurrence of the corresponding nodes in the Bayesian network is determined, and the root cause of the abnormality is finally output. By combining mechanistic modeling and data modeling techniques, it becomes possible to rapidly, automatically, and accurately detect and analyze final rolling temperature anomalies during the rolling process. When applying the system in the field, when compared to manual analysis by onsite personnel, the accuracy of deducing the causes of anomalies was found to reach 92%. |
| first_indexed | 2024-03-11T11:25:20Z |
| format | Article |
| id | doaj.art-6192674e5f2d47e3993ff79c4e7437ed |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-11T11:25:20Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-6192674e5f2d47e3993ff79c4e7437ed2023-11-10T15:07:39ZengMDPI AGMaterials1996-19442023-11-011621702110.3390/ma16217021Digital Twin-Based Fault Diagnosis Platform for Final Rolling Temperature in Hot Strip ProductionChen Desheng0Shao Jian1Li Mingxin2Xiang Sensen3National Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083, ChinaNational Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083, ChinaNational Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083, ChinaNational Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083, ChinaThe final rolling temperature in hot rolling is an important process parameter for hot-rolled strips and greatly influences their mechanical properties and rolling stability. The diagnosis of final rolling temperature anomalies in hot rolling has always been difficult in industry. A data-driven risk assessment method for detecting final rolling temperature anomalies is proposed. In view of the abnormal setting value for the strip head, a random forest model is established to screen the process parameters with high feature importance, and the isolation forest algorithm is used to evaluate the risk associated with the remaining parameters. In view of the abnormal process curve of the full length of the strip, the Hausdorff distance algorithm is used to eliminate samples with large deviations, and a risk assessment of the curve is carried out using the <i>LCSS</i> algorithm. Aiming to understand the complex coupling relationship between the influencing factors, a method for identifying the causes of anomalies, combining a knowledge graph and a Bayesian network, is established. According to the results of the strip head and the full-length risk assessment model, the occurrence of the corresponding nodes in the Bayesian network is determined, and the root cause of the abnormality is finally output. By combining mechanistic modeling and data modeling techniques, it becomes possible to rapidly, automatically, and accurately detect and analyze final rolling temperature anomalies during the rolling process. When applying the system in the field, when compared to manual analysis by onsite personnel, the accuracy of deducing the causes of anomalies was found to reach 92%.https://www.mdpi.com/1996-1944/16/21/7021final rolling temperaturedigital twinknowledge graphhot-rolled stripfault diagnosis |
| spellingShingle | Chen Desheng Shao Jian Li Mingxin Xiang Sensen Digital Twin-Based Fault Diagnosis Platform for Final Rolling Temperature in Hot Strip Production Materials final rolling temperature digital twin knowledge graph hot-rolled strip fault diagnosis |
| title | Digital Twin-Based Fault Diagnosis Platform for Final Rolling Temperature in Hot Strip Production |
| title_full | Digital Twin-Based Fault Diagnosis Platform for Final Rolling Temperature in Hot Strip Production |
| title_fullStr | Digital Twin-Based Fault Diagnosis Platform for Final Rolling Temperature in Hot Strip Production |
| title_full_unstemmed | Digital Twin-Based Fault Diagnosis Platform for Final Rolling Temperature in Hot Strip Production |
| title_short | Digital Twin-Based Fault Diagnosis Platform for Final Rolling Temperature in Hot Strip Production |
| title_sort | digital twin based fault diagnosis platform for final rolling temperature in hot strip production |
| topic | final rolling temperature digital twin knowledge graph hot-rolled strip fault diagnosis |
| url | https://www.mdpi.com/1996-1944/16/21/7021 |
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