Toward Safer Flight Training: The Data-Driven Modeling of Accident Risk Network Using Text Mining Based on Deep Learning
The flight training, a critical component of the general aviation industry, exhibits a relatively high severity of risk due to its complexity and the uncertainty inherent in risk interactions. To mine the risk factors and dynamic evolution characteristics affecting flight safety, a data-driven netwo...
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| Vydáno v: | International journal of computational intelligence systems Ročník 17; číslo 1; s. 1 - 21 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Dordrecht
Springer Netherlands
26.11.2024
Springer |
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| ISSN: | 1875-6883, 1875-6883 |
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| Abstract | The flight training, a critical component of the general aviation industry, exhibits a relatively high severity of risk due to its complexity and the uncertainty inherent in risk interactions. To mine the risk factors and dynamic evolution characteristics affecting flight safety, a data-driven network modeling methodology that integrates text mining with domain knowledge in accident analysis is proposed for the analysis of accident risks specific to flight training. First, flight training accident reports are labeled using domain knowledge gained from accident causation theory to provide basic data for subsequent study. Second, the adversarial training algorithm is introduced to enhance the generalization capability of BERT model in processing imbalanced accident textual data. The fine-tuned BERT, Bidirectional Long Short-Term Memory (Bi-LSTM) Conditional Random Field (CRF) algorithm is fused to construct an ensemble algorithm for risk identification, which accomplishes the joint entity-relationship extraction of accident reports. Third, based on the risk identification results, data-driven modeling of the Flight Training Risk Network (FTRN) is performed to quantify the accident evolution characteristics. Then the aforementioned tasks are meticulously optimized and integrated, subsequently applied to a case study focusing on Loss of Control In-Flight(LOCI) accidents. The findings suggest that the identification algorithm effectively and efficiently extracts risk information and inter-relationships. In addition, the network analysis results reveal the key insights into flight training accidents, facilitating the development of holistic risk control strategies. This study provides a powerful and innovative analytical tool for safety management departments, enhancing safety and reliability in flight training operations. |
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| AbstractList | Abstract The flight training, a critical component of the general aviation industry, exhibits a relatively high severity of risk due to its complexity and the uncertainty inherent in risk interactions. To mine the risk factors and dynamic evolution characteristics affecting flight safety, a data-driven network modeling methodology that integrates text mining with domain knowledge in accident analysis is proposed for the analysis of accident risks specific to flight training. First, flight training accident reports are labeled using domain knowledge gained from accident causation theory to provide basic data for subsequent study. Second, the adversarial training algorithm is introduced to enhance the generalization capability of BERT model in processing imbalanced accident textual data. The fine-tuned BERT, Bidirectional Long Short-Term Memory (Bi-LSTM) Conditional Random Field (CRF) algorithm is fused to construct an ensemble algorithm for risk identification, which accomplishes the joint entity-relationship extraction of accident reports. Third, based on the risk identification results, data-driven modeling of the Flight Training Risk Network (FTRN) is performed to quantify the accident evolution characteristics. Then the aforementioned tasks are meticulously optimized and integrated, subsequently applied to a case study focusing on Loss of Control In-Flight(LOCI) accidents. The findings suggest that the identification algorithm effectively and efficiently extracts risk information and inter-relationships. In addition, the network analysis results reveal the key insights into flight training accidents, facilitating the development of holistic risk control strategies. This study provides a powerful and innovative analytical tool for safety management departments, enhancing safety and reliability in flight training operations. The flight training, a critical component of the general aviation industry, exhibits a relatively high severity of risk due to its complexity and the uncertainty inherent in risk interactions. To mine the risk factors and dynamic evolution characteristics affecting flight safety, a data-driven network modeling methodology that integrates text mining with domain knowledge in accident analysis is proposed for the analysis of accident risks specific to flight training. First, flight training accident reports are labeled using domain knowledge gained from accident causation theory to provide basic data for subsequent study. Second, the adversarial training algorithm is introduced to enhance the generalization capability of BERT model in processing imbalanced accident textual data. The fine-tuned BERT, Bidirectional Long Short-Term Memory (Bi-LSTM) Conditional Random Field (CRF) algorithm is fused to construct an ensemble algorithm for risk identification, which accomplishes the joint entity-relationship extraction of accident reports. Third, based on the risk identification results, data-driven modeling of the Flight Training Risk Network (FTRN) is performed to quantify the accident evolution characteristics. Then the aforementioned tasks are meticulously optimized and integrated, subsequently applied to a case study focusing on Loss of Control In-Flight(LOCI) accidents. The findings suggest that the identification algorithm effectively and efficiently extracts risk information and inter-relationships. In addition, the network analysis results reveal the key insights into flight training accidents, facilitating the development of holistic risk control strategies. This study provides a powerful and innovative analytical tool for safety management departments, enhancing safety and reliability in flight training operations. |
| ArticleNumber | 291 |
| Author | Wang, Lei Zhuang, Zibo Hou, Yongkang Yang, Lei Gong, Jingwei |
| Author_xml | – sequence: 1 givenname: Zibo surname: Zhuang fullname: Zhuang, Zibo organization: The College of Air Traffic Management, Civil Aviation University of China – sequence: 2 givenname: Yongkang surname: Hou fullname: Hou, Yongkang organization: The College of Safety Science and Engineering, Civil Aviation University of China – sequence: 3 givenname: Lei surname: Yang fullname: Yang, Lei organization: The Flight Academy, Civil Aviation University of China – sequence: 4 givenname: Jingwei surname: Gong fullname: Gong, Jingwei organization: The College of Safety Science and Engineering, Civil Aviation University of China – sequence: 5 givenname: Lei orcidid: 0000-0001-8147-0554 surname: Wang fullname: Wang, Lei email: wanglei0564@hotmail.com organization: The College of Safety Science and Engineering, Civil Aviation University of China |
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| SubjectTerms | Accident analysis Artificial Intelligence Aviation accident Complex network Computational Intelligence Control Engineering Mathematical Logic and Foundations Mechatronics Research Article Risk identification Robotics Text mining |
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| Title | Toward Safer Flight Training: The Data-Driven Modeling of Accident Risk Network Using Text Mining Based on Deep Learning |
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