基于飞行数据的MSCNN-LSTM水平安定面系统状态监测方法  

MSCNN-LSTM method for monitoring the state of horizontal stabilizer system based on flight data

作　　者：张鹏 胡芳语 段照斌 刘静静 ZHANG Peng;HU Fangyu;DUAN Zhaobin;LIU Jingjing(Engineering Techniques Training Center,CAUC,Tianjin 300300,China;College of Electronic Information Engineering,CAUC,Tianjin 300300,China)

机构地区：[1]中国民航大学工程技术训练中心,天津300300 [2]中国民航大学电子信息与自动化学院,天津300300

出　　处：《中国民航大学学报》2025年第1期60-66,82,共8页Journal of Civil Aviation University of China

基　　金：中国航空工业集团公司西安飞行自动控制研究所项目(H04420180028)。

摘　　要：针对真实飞行数据中故障样本匮乏、数据类间失衡且缺少标注问题,本文提出了一种基于多尺度卷积神经网络(MSCNN,multi-scale convolutional neural network)与长短时记忆(LSTM,long short-term memory)网络的水平安定面系统状态监测方法。此方法不依赖于标注数据,利用无监督学习的方式对水平安定面系统进行状态监测。首先,利用MSCNN-LSTM对系统正常运行状态的快速存储记录器(QAR,quick access recorder)数据从空间和时间两个维度进行特征提取,以实现舵面位置预测;其次,计算舵面位置预测值与舵面位置实际值的残差,分析残差分布来确定系统健康状态的阈值;最后,利用某飞机的QAR数据进行验证。实验结果表明,本文所提方法能准确实现水平安定面系统飞行级的异常状态识别,并能在系统发生故障时,提前1个飞行循环进行异常预警。To address the problems of insufficient fault samples,imbalanced data classes and lack of labeling in real flight da-ta,a state monitoring method for a horizontal stabilizer system based on multi-scale convolutional neural network(MSCNN)and long short-term memory(LSTM)network is proposed in this paper.This method does not rely on la-beled data and uses unsupervised learning to monitor the state of the horizontal stabilizer system.Firstly,the quick access recorder(QAR)data of the system in normal operation are extracted in both spatial and temporal dimensions using MSCNN-LSTM to achieve rudder position prediction.Secondly,the residuals between the predicted and ac-tual values of the rudder position are calculated and the distribution of the residuals is analyzed to determine the threshold for the health state of the system,Finally,the QAR data of an aircraft is used for verification,and the ex-perimental results show that the proposed method in this paper can accurately achieve the abnormal state identifi-cation of the horizontal stabilizer system at the flight level and can provide an abnormal alarm one flight cycle in advance when system failure occurring.

关 键 词：飞行数据 MSCNN-LSTM 水平安定面 状态监测 无监督学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]