论文标题
NGAFID的大规模注释的多元时间序列航空维护数据集
A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID
论文作者
论文摘要
本文介绍了最大的公开，未模拟的，范围内的飞机飞行记录和维护日志数据，用于预测零件故障和维护需求。我们在28,935次航班上展示了31,177小时的飞行数据，这些航班相对于2,111个计划外的维护活动聚集在36种类型的维护问题中。像维护之前或之后一样对航班进行注释，并在维护当天进行一些航班。收集数据来评估预测维护系统是具有挑战性的，因为从折衷的飞机中生成数据是困难，危险和不道德的。为了克服这一点，我们使用国家通用航空飞行信息数据库（NGAFID），该数据库包含飞机正常运行期间记录的航班以及维护日志来构建零件故障数据集。我们使用剩余有用寿命（RUL）预测的新颖框架，并考虑零件统治大于2天的可能性。与以前使用模拟或实验室设置生成的数据集不同，NGAFID航空维护数据集包含来自不同季节，天气状况，飞行员和飞行模式的真实飞行记录和维护日志。此外，我们还提供Python代码，以轻松下载数据集和COLAB环境，以在三种不同的模型上重现我们的基准。我们的数据集为机器学习研究人员带来了一个艰巨的挑战，也是测试和开发预后健康管理方法的宝贵机会
This paper presents the largest publicly available, non-simulated, fleet-wide aircraft flight recording and maintenance log data for use in predicting part failure and maintenance need. We present 31,177 hours of flight data across 28,935 flights, which occur relative to 2,111 unplanned maintenance events clustered into 36 types of maintenance issues. Flights are annotated as before or after maintenance, with some flights occurring on the day of maintenance. Collecting data to evaluate predictive maintenance systems is challenging because it is difficult, dangerous, and unethical to generate data from compromised aircraft. To overcome this, we use the National General Aviation Flight Information Database (NGAFID), which contains flights recorded during regular operation of aircraft, and maintenance logs to construct a part failure dataset. We use a novel framing of Remaining Useful Life (RUL) prediction and consider the probability that the RUL of a part is greater than 2 days. Unlike previous datasets generated with simulations or in laboratory settings, the NGAFID Aviation Maintenance Dataset contains real flight records and maintenance logs from different seasons, weather conditions, pilots, and flight patterns. Additionally, we provide Python code to easily download the dataset and a Colab environment to reproduce our benchmarks on three different models. Our dataset presents a difficult challenge for machine learning researchers and a valuable opportunity to test and develop prognostic health management methods
