Aviation is a complicated transportation system, and safety is of paramount importance because aircraft failure often involves casualties. Prevention is clearly the best strategy for aviation transportation safety. Learning from past incident data to prevent potential accidents from happening has proved to be a successful approach. To prevent potential safety hazards and make effective prevention plans, aviation safety experts identify primary and contributing factors from incident reports. However, safety experts’ review processes have become prohibitively expensive nowadays. The number of incident reports is increasing rapidly due to the acceleration of advances in information technologies and the growth of the commercial and private aviation transportation industries. Consequently, advanced text mining algorithms should be applied to help aviation safety experts facilitate the process of incident data extraction. This paper focuses on constructing deep-learning-based models to identify causal factors from incident reports. First, we prepare the data sets used for training, validation, and testing with approximately 200,000 qualified incident reports from the Aviation Safety Reporting System (ASRS). Then, we take an open-source natural language model, which is well trained with a large corpus of Wikipedia texts, as the baseline and fine-tune it with the texts in incident reports to make it more suited to our specific research task. Finally, we build and train an attention-based long short-term memory (LSTM) model to identify primary and contributing factors in each incident report. The solution we propose has multilabel capability and is automated and customizable, and it is more accurate and adaptable than traditional machine learning methods in extant research. This novel application of deep learning algorithms to the incident reporting system can efficiently improve aviation safety.

中文翻译：

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确定事故原因以提高航空运输安全：提出深度学习方法

航空是一个复杂的运输系统，安全至关重要，因为飞机故障往往会造成人员伤亡。预防显然是航空运输安全的最佳策略。从过去的事故数据中学习以防止潜在事故的发生已被证明是一种成功的方法。为预防安全隐患，制定有效的预防计划，航空安全专家从事故报告中识别主要和影响因素。然而，如今，安全专家的审查过程已经变得非常昂贵。由于信息技术进步的加速以及商业和私人航空运输业的增长，事故报告的数量正在迅速增加。最后，应应用先进的文本挖掘算法来帮助航空安全专家促进事件数据提取过程。本文侧重于构建基于深度学习的模型，以从事件报告中识别因果因素。首先，我们准备用于培训、验证和测试的数据集，其中包含来自航空安全报告系统 (ASRS) 的大约 200,000 份合格事件报告。然后，我们采用一个开源自然语言模型，该模型经过大量维基百科文本的训练，作为基线，并使用事件报告中的文本对其进行微调，使其更适合我们的特定研究任务。最后，我们构建并训练了一个基于注意力的长短期记忆 (LSTM) 模型，以识别每个事件报告中的主要和影响因素。我们提出的解决方案具有多标签能力，并且是自动化和可定制的，并且比现有研究中的传统机器学习方法更准确和适应性更强。深度学习算法在事故报告系统中的这种新颖应用可以有效地提高航空安全。