基于QAR数据的飞行控制系统故障研究综述

A review of flight control system fault research based on QAR data

为系统梳理国内外对民用飞机飞行控制系统故障分析的研究历程和现状,针对基于快速存取记录器(QAR)数据分析的飞行控制系统典型故障类型,首先,总结QAR数据预处理、特征提取等使用过程;然后,根据故障分析可达到的性能指标,提出4个故障研究阶段,分别为故障监测、故障识别、故障诊断和故障预测;最后,综合国内外研究进度与深度,得出飞行控制系统典型故障类型,包括方向舵液压泄漏、升降舵指示不一致、襟翼动作耗时等,建模常用QAR数据项包括飞机主舵面位置、飞行姿态、飞机性能、左右襟翼角度、襟翼位置等,计算方法包括物理模型、多变量统计、逻辑推理、机器学习等。结果表明:系统分析方向舵、升降舵、襟翼等子系统最新研究进展,发现在故障类型、参数选择和计算方法的改进等方面取得了一定的成果,故障研究阶段基本处于故障诊断或非实时预测水平,但仍需加强面向安全保障与实际维修方面的需求,以实现故障实时预测技术。

To systematically review the research progress and current status of fault analysis in civil aircraft flight control systems,both domestically and internationally,this review study was carried out.The study focused on identifying typical fault types of flight control systems through analysing QAR data.Firstly,the process of QAR data preprocessing and feature extraction was summarized.Secondly,based on the performance metrics achievable by fault analysis,four stages of fault research were proposed,including fault monitoring,fault identification,fault diagnosis,and fault prediction.Finally,by combining the progress and depth of domestic and international research,typical fault types of flight control systems were identified,including rudder hydraulic leakage,inconsistent elevator indications,and flap actuation delays.Commonly used QAR data items for modeling include aircraft primary control surface positions,flight attitudes,aircraft performance,left and right flap angles,and flap positions.Calculation methods encompass physical models,multivariate statistics,logical reasoning,and machine learning.The results show that through a systematic analysis of the latest research progress in subsystems such as rudder,elevator,and flaps,it is found that certain achievements have been made in fault types,parameter selection,and the improvement of calculation methods.However,the fault research stage is primarily focused on fault diagnosis or non-real-time prediction.Further emphasis is required on addressing safety assurance and practical maintenance needs to achieve real-time fault prediction technology.