自回归积分移动平均
建筑
航空
可靠性工程
计算机科学
变压器
工程类
航空安全
时间序列
机器学习
航空航天工程
地理
电气工程
考古
电压
作者
Hang Zeng,Hongmei Zhang,Jiansheng Guo,Bo Ren,Lijie Cui,Jiangnan Wu
标识
DOI:10.1016/j.ress.2024.110089
摘要
Accurate prediction of aviation failure events helps to anticipate future safety situations and protect against further uncontrollable accidents. However, the large sample size, complex temporal characteristics, and significant long-term correlation of aviation failure events increase the operational cost of accurate prediction. To address these challenges, this paper proposes a novel approach involving seasonal-trend decomposition using Loess (STL) and a hybrid prediction model consisting of a transformer and autoregressive integrated moving average (ARIMA). First, STL decomposition is utilized to isolate trend, seasonal, and remainder components, contributing to a comprehensive understanding of the events sample characteristics. The trend component is then trained and predicted using transformer, solving the vanishing gradient problem and improving computational efficiency. ARIMA is employed to train and predict the seasonal and remainder components, maintaining accuracy while reducing complexity. Finally, a comparative evaluation between the proposed and multiple existing approaches is conducted using Aviation Safety Reporting System (ASRS) data. The results demonstrate that the STL-transformer-ARIMA provides more accurate predictions of failure events than single model. It also exhibits significant advantages in robustness and generalization capacity compared to single transformer-based predictors. This revealed that the proposed approach performed better in predicting aviation failure events.
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