基于机器学习的飞机动力装置运行可靠性

doi:10.7527/S1000-6893.2020.24732

固体力学与飞行器总体设计

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基于机器学习的飞机动力装置运行可靠性

冯蕴雯¹, 潘维煌¹, 刘佳奇¹, 路成¹, 薛小锋¹, 冷佳醒²

1. 西北工业大学航空学院, 西安 710072;
2. 上海微小卫星工程中心导航技术研究所, 上海 201203

收稿日期:2020-09-08 修回日期:2020-10-14 发布日期:2020-10-30
通讯作者: 潘维煌 E-mail:panweihuang8@163.com
基金资助:
国家自然科学基金（51875465）

Operational reliability of aircraft power plant based on machine learning

FENG Yunwen¹, PAN Weihuang¹, LIU Jiaqi¹, LU Cheng¹, XUE Xiaofeng¹, LENG Jiaxing²

1. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Institute of Navigation Technology, Shanghai Engineering Center for Microsatellites, Shanghai 201203, China

Received:2020-09-08 Revised:2020-10-14 Published:2020-10-30
Supported by:
National Natural Science Foundation of China (51875465)

摘要/Abstract

摘要： 为了研究分析飞机的动力装置在执行飞行任务过程中的运行可靠性，针对运行可靠性影响因素的多维、耦合的特点，采用机器学习方法对动力装置运行可靠性的时变规律及其相关影响因素进行分析。提出了考虑动力装置的工作状态、飞机的运行外界条件、飞机的飞行状态3类因素分析动力装置实时运行状态下的时变可靠性方法；并基于飞机实际运行的快速存取记录器（QAR）数据，梳理了动力装置运行可靠性分析相关的3类因素、16个主要特征。结合飞机运行的时空关系，采用数据包络分析（DEA）方法对飞机动力装置的工作状态特性与性能裕度进行非参数分析，基于提取的QAR数据特征，采用随机森林、多变量神经网络回归算法，建立2种基于机器学习的动力装置运行可靠性分析模型。以B737-800机型为例，对一次北京至珠海的飞行任务的动力装置相关运行数据进行分析，对2种机器学习分析模型进行训练与测试研究。分析结果表明：对动力装置工作状态特性贡献度最大的特征依次为计算空速、飞行时间与飞行高度；对动力装置性能裕度贡献度最大的特征依次为动力装置工作状态特性、雷达气象与飞行时间。所采用的2种机器学习方法能较好反映动力装置运行过程的时变可靠性规律，可为动力装置的运行与特情处理提供参考。

关键词: 运行可靠性, 数据包络分析, 随机森林, 神经网络, QAR运行数据

Abstract: To study the operational reliability of aircraft power plants during flight missions, we analyze the time-varying law and related influencing factors of power plant operational reliability using the machine learning method, meanwhile considering the multi-dimensional and coupling characteristics influencing the operational reliability. An operational reliability analysis method is proposed for power plants considering three factors: the operating state of the power plant, the operating state of the aircraft, and the operating environment of the power plant. Based on the QAR (Quick Access Recorder) data of the actual operation of the aircraft, this method identifies three kinds of factors and 16 main characteristics related to the operational reliability analysis of the power plant. Combined with the space-time relationship of aircraft operation, non-parametric analysis of the working state characteristics and the performance margin of aircraft power plants is conducted using DEA (Data Envelopment Analysis). According to the proposed QAR data characteristics, the random forest and multivariable neural network regression algorithm is used to establish two kinds of operational reliability analysis models of power plants based on machine learning. Taking B737-800 aircraft as an example, this paper analyzes the power plant operation data of a flight mission from Beijing to Zhuhai, and studies the training and testing of two machine learning analysis models. The analysis results show that the features contributing most to the power plant operating state characteristics are calculated airspeed, flight time, and flight altitude; those to the power plant performance margin are power plant operating state characteristics, radar weather, and flight time. The two types of machine learning methods proposed can well reflect the time-varying reliability law of the power plant operation process, providing reference for power plant operation and special situation handling.

Key words: operational reliability, data envelopment analysis, random forest, neural networks, QAR operation data

中图分类号:

V328.5
U8

冯蕴雯, 潘维煌, 刘佳奇, 路成, 薛小锋, 冷佳醒. 基于机器学习的飞机动力装置运行可靠性[J]. 航空学报, 2021, 42(4): 524732-524732.

FENG Yunwen, PAN Weihuang, LIU Jiaqi, LU Cheng, XUE Xiaofeng, LENG Jiaxing. Operational reliability of aircraft power plant based on machine learning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524732-524732.

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于机器学习的飞机动力装置运行可靠性

Operational reliability of aircraft power plant based on machine learning

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