Dynamic Interactional And Cooperative Network For Shield Machine
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866910612717568000 |
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| author | Gao, Dazhi Li, Rongyang Wang, Hongbo Mao, Lingfeng Ning, Huansheng |
| author_facet | Gao, Dazhi Li, Rongyang Wang, Hongbo Mao, Lingfeng Ning, Huansheng |
| contents | The shield machine (SM) is a complex mechanical device used for tunneling. However, the monitoring and deciding were mainly done by artificial experience during traditional construction, which brought some limitations, such as hidden mechanical failures, human operator error, and sensor anomalies. To deal with these challenges, many scholars have studied SM intelligent methods. Most of these methods only take SM into account but do not consider the SM operating environment. So, this paper discussed the relationship among SM, geological information, and control terminals. Then, according to the relationship, models were established for the control terminal, including SM rate prediction and SM anomaly detection. The experimental results show that compared with baseline models, the proposed models in this paper perform better. In the proposed model, the R2 and MSE of rate prediction can reach 92.2\%, and 0.0064 respectively. The abnormal detection rate of anomaly detection is up to 98.2\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_10473 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Dynamic Interactional And Cooperative Network For Shield Machine Gao, Dazhi Li, Rongyang Wang, Hongbo Mao, Lingfeng Ning, Huansheng Machine Learning Systems and Control Signal Processing The shield machine (SM) is a complex mechanical device used for tunneling. However, the monitoring and deciding were mainly done by artificial experience during traditional construction, which brought some limitations, such as hidden mechanical failures, human operator error, and sensor anomalies. To deal with these challenges, many scholars have studied SM intelligent methods. Most of these methods only take SM into account but do not consider the SM operating environment. So, this paper discussed the relationship among SM, geological information, and control terminals. Then, according to the relationship, models were established for the control terminal, including SM rate prediction and SM anomaly detection. The experimental results show that compared with baseline models, the proposed models in this paper perform better. In the proposed model, the R2 and MSE of rate prediction can reach 92.2\%, and 0.0064 respectively. The abnormal detection rate of anomaly detection is up to 98.2\%. |
| title | Dynamic Interactional And Cooperative Network For Shield Machine |
| topic | Machine Learning Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2211.10473 |