%0 Journal Article
%T 航空器预测性维修：数据探索与可解释数据模型的结合<br>Aircraft Predictive Maintenance: A Combination of Data Exploration and Explainable Data Models
%A 张逸俊
%A 葛雅静
%A 刘鹏飞
%J Statistics and Applications
%P 1367-1375
%@ 2325-226X
%D 2023
%I Hans Publishing
%R 10.12677/SA.2023.125141
%X 在航空器维修领域，数据驱动的预测性维修方法正在不断发展，以提高飞机的安全性和效率。通过数据收集与处理，进行数据探索和可视化，然后对模型开发与验证，实现模型部署与监控，最后预测性维修策略制定和反馈、优化和记录。探讨数据驱动的航空器预测性维修方法，特别是数据探索和可解释数据模型的结合，以期提供一种更高效和可靠的飞机维修解决方案，为构建预测性维修平台提供相应参考和指导。<br />
In the field of aircraft maintenance, data-driven predictive maintenance methods are constantly evolving to improve aircraft safety and efficiency. Through data collection and processing, data exploration and visualization are carried out, followed by model development and validation to achieve model deployment and monitoring. Finally, predictive maintenance strategies are formulated and fed back, optimized, and recorded. This paper discusses the method of data-driven aircraft predictive maintenance, especially the combination of data exploration and interpretable data model, in order to provide a more efficient and reliable aircraft maintenance solution and provides the corresponding reference and guidance for the construction of predictive maintenance platform.
%K 航空器维修，预测性维修，数据模型，可视化分析<br>Aircraft Maintenance
%K Predictive Maintenance
%K Data Model
%K Visual Analytics
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=74395