航空发动机排气温度测量通道故障智能检测方法研究

EGT（Exhaust Gas Temperature,排气温度）测量通道故障是一种典型的航空发动机测量系统故障,如果试验过程中不能及时发现,就存在不能及时排除EGT超温、发动机喘振、发动机异常停车等故障的可能,造成重大安全隐患。通过对EGT测量通道故障现象和数据的分析,提出了一种EGT测量通道故障模糊神经网络检测算法。该算法将高压转速偏差、低压转速偏差和排气温度偏差作为模糊逻辑系统的输入参数,通过模糊推理实现故障检测。高压转速、低压转速和排气温度的估计值通过试飞数据训练的神经网络估计模型获得。理论分析和试飞数据验证结果表明,该方法具备实时检测EGT测量通道故障的能力,且具有良好的稳定性,可以满足工程应用要求。     

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Deep learning techniques have outstanding performance in feature extraction and model fitting. In the field of aero-engine fault diagnosis, the introduction of deep learning technology is of great significance. The aero-engine is the heart of the aircraft, and its stable operation is the primary guarantee of the aircraft. In order to ensure the normal operation of the aircraft, it is necessary to study and diagnose the faults of the aero-engine. Among the many engine failures, the one that occurs more frequently and is more hazardous is the wheeze, which often poses a great threat to flight safety. On the basis of analyzing the mechanism of aero-engine surge, an aero-engine surge fault diagnosis method based on deep learning technology is proposed. In this paper, key sensor data are obtained by analyzing different engine sensor data. An aero-engine surge dataset acquisition algorithm (ASDA) is proposed to sample the fault and normal points to generate the training set, validation set and test set. Based on neural network models such as one-dimensional convolutional neural network (1D-CNN), convolutional neural network (RNN), and long-short memory neural network (LSTM), different neural network optimization algorithms are selected to achieve fault diagnosis and classification. The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis. The aero-engine surge fault diagnosis network (ASFDN) proposed in this paper achieves better results. Through training, the network achieves more than 99% classification accuracy for the test set.     

Design of robust redundancy relations for a semi-scale YF-22 aircraft model

This paper presents an “ad hoc” methodology for the design of diagnostic software for the detection and isolation of faults on sensors and actuators of a remotely controlled semi scale YF-22 research aircraft. Starting from the structural analysis of the nonlinear dynamic equations of the aircraft, an algorithm, based on the “variables elimination method”, is proposed to compute a set of residual equations having all the possible fault signatures. The quality of each residual equation has been ranked according to a cost function chosen to represent implementation issues such as the sensitivity to measurement noise in the numerical computation of high order derivatives. An algorithm is then proposed for selecting a subset of residual equations with maximum “failure isolability” and minimum cost, according to the selected performance criteria. The issue of robustification of the residual equations to modeling errors and measurement noise has been addressed through nonlinear uncertainty mapping using Neural Networks in conjunction to FIR filters. The fault detection and isolation method has been applied by injecting simulated faults to flight data collected by a semi-scale YF-22 research aircraft model.     

非线性去噪对发动机排气温度预测精度影响

针对传统方法难以进一步提高发动机排气温度(EGT)预测精度的问题,提出一种基于非线性去除噪声的正则化混沌预测算法.为了消除其他因素和离群点对EGT预测精度的影响,研究了实际测量数据的预处理方法.由于观测数据中的噪声和混沌相空间重构参数对EGT预测精度有较大影响,提出利用搜索算法寻找使EGT预测精度较优的非线性去噪参数和重构相空间的延迟时间和嵌入维变量.仿真结果表明,该算法用于某型航空发动机EGT序列的预测,获得了满意的结果.     

基于改进ARMA模型的火箭发动机稳态工况过程实时故障诊断方法研究

针对大推力氢氧补燃循环发动机的主级工况,设计了基于ARMA模型的实时故障诊断算法,并改进了阈值求解方法以及故障判别准则,通过仿真验证,证明了改进算法的高效性及有效性,为建立大推力氢氧补燃循环发动机健康监控系统奠定了基础。首先,建立了大推力氢氧补燃循环发动机的故障模型,得到了典型故障的故障数据;接着,设计了改进的ARMA模型、阈值求解算法和故障判别准则;最后进行仿真分析,结果表明改进算法能够在毫秒的量级诊断出各典型故障,满足了发动机故障诊断系统设计的要求。     

基于优化配平的机翼故障飞机飞行性能分析

分析飞机机翼故障对飞行性能的影响,对飞机故障后能够安全着陆或返航有着重要意义,飞机的机翼作为产生力和力矩的主要部件对飞行性能起着重要的作用。提出一种基于单纯形优化的机翼故障飞机飞行性能分析方法,建立机翼故障参数模型,根据飞机爬升转弯飞行条件进行优化配平计算,得到在不同状态下不同机翼故障的配平数据库,分析了故障后飞机的飞行性能。仿真结果表明所提算法的有效性。     

一种基于最小平均距离的测量飞机航迹规划算法

以测量飞机对单个低空飞行器跟踪为研究对象,根据大地坐标系统和椭球几何学基本原理,结合机载测量设备的作用范围,提出一种基于最小平均距离的测量飞机航迹规划算法.分析预定的低空飞行器飞行路线,提取低空飞行器飞行关键点,划分出低空飞行器水平飞行阶段和转弯飞行阶段,按阶段分别规划出测量飞机的航迹,保证对低空飞行器全程连续稳定跟踪的同时,使测量飞机与低空飞行器的平均距离最小,以提高测量质量,并模拟低空飞行器3种飞行路线,规划了相应的测量飞机航迹,经Mat-lab仿真分析,规划结果满足设计要求,验证了算法的有效性.     

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault‐tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is still a challenging problem. In this paper, an intelligent neural network‐based model predictive control structure is proposed, where the prediction model is updated in each iteration based on a novel proposed online sequential multimodel structure. A hybrid offline‐online learning algorithm is adopted in the introduced online sequential multimodel structure to identify the time‐varying dynamics of the system. The proposed control structure can satisfactorily deal with unexpected actuator faults and structural damages as well as unmodeled dynamics and wind turbulence. The stability of the closed‐loop system is proved under some realistic assumptions. The simulation results demonstrate the high capability of the proposed approach for trajectory tracking control of a conventional aircraft in the simultaneous presence of system faults and external disturbances.     

Data‐based design of robust fault detection and isolation residuals via LASSO optimization and Bayesian filtering

In this paper, a data‐based approach for the design of structured residual subsets for the robust isolation of sensor faults is proposed. Linear regression models are employed to estimate faulty signals and to build a set of primary residuals. L₁‐regularized least squares estimation is used to identify model parameters and to enforce sparsity of the solutions by increasing the regularization weight. In this way, it is possible to generate a set of residuals generators with different fault sensitivity. Then, a residual selection procedure based on fault sensitivity maximization is proposed to extract a minimum size subset of structured residuals that allows for isolation of the faulty sensor. To overcome modelling uncertainty, a robust recursive Bayesian Filter has been employed to process, online, the distance of the residuals from nominal fault directions, providing a fault probability for each sensor. The proposed method has been validated by designing and testing a fault isolation scheme for six aircraft sensors using multi‐flight experimental data of a P92 Tecnam aircraft.     

基于粒子群算法的SVM飞机空调系统状态评估

飞机空调系统对飞机和旅客都起着至关重要的作用,对飞机QAR(Quick Access Recorder)空调数据的健康评价进行预测,可以保证乘客和机组的飞行舒适性、安全性,以及电子电气设备工作的稳定性,避免机械故障导致的航班延误或取消。为提高空调系统状态监控SVM模型预测的准确度,提出了一种基于粒子群算法的SVM空调状态评估方法。通过实验结果可知,使用A320飞机空调系统状态监控收集的样本数据进行预测分析,提出的方法能够有效评估空调系统状态。     

基于XGBoost的民航飞机发动机性能参数预测模型

为提高民航飞机发动机性能参数的预测精度,本文提出一种基于模糊推理和XGBoost算法的发动机性能参数预测方法。对发动机进行总体性能分析,确定油门杆位置、气压高度、总温、全重、马赫数及飞行阶段为影响发动机性能参数的主要因素。其次采用模糊推理对快速存取记录器（QAR）数据进行纵向飞行阶段划分,消除人为划分训练数据对预测精度的主观影响。最后,建立各发动机性能参数的XGBoost预测模型,并与多种预测模型进行对比实验。实验结果表明：对发动机N1、燃油流量参数的预测,XGBoost预测模型相比支持向量回归（SVM）、线性回归模型和BP神经网络,其精度更高且不需要对训练数据进行缩放。     

基于发参数据的发动机泵调系统的健康监测

为探究飞机发动机泵调系统健康监测自动化的可行性,开展了基于发参数据的飞机发动机泵调系统的健康监测方法的研究.从发参数据中提取出泵调系统在起动阶段的相关特征参数,构成样本数据;采用马氏距离技术筛选出健康样本,建立泵调系统特征参数基线模型;计算实际飞行样本与基线模型之间的马氏距离作为健康指数,确定预警阈值.采用3种K均值聚...     

Double-model adaptive fault detection and diagnosis applied to real flight data

The existing multiple model-based estimation algorithms for Fault Detection and Diagnosis (FDD) require the design of a model set, which contains a number of models matching different fault scenarios. To cope with partial faults or simultaneous faults, the model set can be even larger. A large model set makes the computational load intensive and can lead to performance deterioration of the algorithms. In this paper, a novel Double-Model Adaptive Estimation (DMAE) approach for output FDD is proposed, which reduces the number of models to only two, even for the FDD of partial and simultaneous output faults. Two Selective-Reinitialization (SR) algorithms are proposed which can both guarantee the FDD performance of the DMAE. The performance is tested using a simulated aircraft model with the objective of Air Data Sensors (ADS) FDD. Another contribution is that the ADS FDD using real flight data is addressed. Issues related to the FDD using real flight test data are identified. The proposed approaches are validated using real flight data of the Cessna Citation II aircraft, which verified their effectiveness in practice.     

航空发动机传感器故障诊断

研究航空发动机传感器故障诊断问题,由于发动机传感器故障样本有限、小样本、非线性变化特点,传统大样本传统故障方法故障诊断准确率低。为提高传感器故障诊断准确率,提出一种混沌粒子群算法(CPSO)和最小二乘支持向量机(LSS-VM)相结合的传感器故障诊断算法(CPSO-LSSVM)。首先将发动机传感器信号输入到LSSVM进行学习,并采用CPSO进行优化,找到最优LSSVM参数,从而建立传感器故障诊断模型,最后采用已建立模型对传感器故障进行仿真测试。仿真结果表明,CPSO-LSSVM提高了航空发动机传感器故障诊断的准确率,能准确地对空发动机传感器故障进行诊断,提供民飞行安全性能保障。     

基于AFSMC算法的飞机飞行姿态自适应滑模控制系统

传统飞机飞行姿态滑膜控制系统,存在飞机飞行姿态自适应系数稳定性差的问题,在控制过程中会受到多重因素影响,导致飞行姿态可控误差系数增大,需要辅助控制系统修正才能完成飞行姿态的控制操作;针对上述问题,提出基于AFSMC算法的飞机飞行姿态自适应滑模控制系统;系统硬件基于PID自适应滑模控制器,对飞机飞行姿态控制器进行结构设计;软件部分通过引入自适应滑模控制策略,对PID控制器姿态控制变量进行适配;引入AFSMC算法计算姿态控制器当前时间点下的运动控制方程,得到飞行姿态自适应滑模控制的最优量,完成基于AFSMC算法的飞机飞行姿态自适应滑模控制系统设计;实验结果表明,所设计系统能够在不同飞行工况下,对飞机飞行姿态作出准确控制,系统的整体控制精度范围为90%~97.4%,飞机飞行控制稳定性较好,有效提升了系统对飞机飞行姿态的控制准确度。     

Chaotic Analysis and Control of Longitudinal Flight Dynamics

Aircraft are considered to be sub‐stable within the flight envelope. This paper presents a chaotic analysis and two kinds of corresponding control methods of aircraft longitudinal flight dynamics. First, the chaotic behavior of the open loop longitudinal flight system is confirmed by the Poincaré section and the Lyapunov exponent. Studies have found that the flight envelope can be divided into a stable area and a sub‐stable area. The probability of instability caused by chaos exists in the sub‐stable area. The definition of the chaotic boundary is also given. Second, based on the chaotic analysis results and considering three different combinations of aircraft mass and elevator deflection, a linear feedback control (LFC) method is implemented to stabilize the chaotic boundary and enlarge the stable area. Third, taking system uncertainties into consideration, an adaptive backstepping sliding mode control (AB‐SMC) method is applied to the trajectory tracking problem of aircraft longitudinal motion, which is based on classic SMC, backstepping and dynamic surface methods. The numerical simulations are given, which can demonstrate the applicability of the proposed control methods.     

基于MRF模型的飞机飞行动作识别划分算法

军用飞机飞行动作具有较强的随机性和模糊性,为实现针对军用飞机飞行动作的识别和划分,提出了一种基于马尔可夫随机场M RF模型的飞行动作识别划分算法,可以在没有标定的情况下,将飞行数据段分割聚类,实现飞行动作的识别和划分.仿真实验表明,相比于传统的飞行动作识别算法,基于M RF模型的飞行动作识别划分算法且有更高的识别率.     

基于FlightGear的A-SMGCS场面活动三维仿真

针对A-SMGCS系统场面活动三维仿真问题,提出一种以开源模拟飞行器FlightGear为场景仿真平台,以实时ADS-B监视数据为驱动,以Linux为操作平台的机场场面活动三维仿真系统设计新方法。仿真系统按照实际机场模型进行机场布局设计,飞机、航站楼、塔台三维建模,地形数据生成,对真实机场环境进行了完整建模。由于ADS-B监视数据缺少飞机姿态信息,提出一种根据位置数据推算出姿态信息的新方法。首先把ADS-B监视数据进行航迹卡尔曼滤波,然后根据飞机前后两个位置的空间连线矢量计算飞行姿态。通过FlightGear多机网络数据接口导入飞机定位数据与姿态数据驱动飞机模型运动,实现了对飞机运动的六自由度仿真。仿真结果表明,该系统能逼真、准确再现真实机场飞机实时活动情况。由于完全基于开源软件设计,该系统低成本实现了对场面活动的实时三维仿真。     

基于改进插值边缘算子的飞机故障模型表示

飞机故障预警对于飞行安全具有重要意义,而实现故障预警的重要前提是要建立正确的故障模型。针对这个问题,结合飞行数据的特殊性,提出了一种基于改进插值边缘算子的飞机故障模型表示方法。该方法首先计算出飞行数据序列上各点的边缘强度和插值误差,运用改进的插值边缘算子算法选取出飞机故障模型的边缘点。实验结果表明,该方法不仅保留了飞行数据序列的主要形态,而且也保留了重要极小值点,具有更小的拟合误差,达到正确建立故障模型的目的。     

Fault estimation for aero‐engine LPV systems based on LFT

In this paper, a fault estimator with linear fractional transformation (LFT) parameter dependency is designed for the linear parameter‐varying (LPV) system of the aero‐engine with both sensor and actuator faults under disturbances. After an aero‐engine affine parameter‐dependent LPV model is derived by the linear fitting method and matrix pseudo‐inverse method, the LPV model with disturbances and fault signals is transformed into a LFT structure. Based on the full block S‐procedure, the sufficient condition for the existence of the fault estimator is proposed, which can lead to less conservative results. Then the fault estimator design algorithm which can adjust to the current system dynamic adaptively is presented. Finally, a fault estimator is designed for a turbofan aero‐engine under multiple types of faults and disturbances to demonstrate the effectiveness of the proposed method.