航空发动机双重传感器故障诊断逻辑研究

针对航空发动机控制系统的双重传感器故障,提出了一种采用双路容错设计的卡尔曼滤波器故障检测隔离系统.故障检测隔离系统由一系列卡尔曼滤波器组成,每个滤波器都假定2路传感器故障,而以故障支路外的测量值作为输入量.当双重传感器故障发生时,只有不包含故障传感器信息的滤波器保持较低的估计残差,其他滤波器都会产生较大的估计残差,如此双重传感器故障便可以被隔离.利用滤波器组估计残差的特征,进一步设计合理的运算逻辑,系统就可以同时对传感器单一故障进行检测和隔离.为了验证故障诊断系统的有效性,在发动机慢车状态分别对传感器发生双重故障和单一故障的情况进行仿真.仿真结果表明:故障诊断系统能够准确有效地对传感器双重故障和单一故障进行检测和隔离.      

基于扩展卡尔曼滤波的动量轮故障检测方法

 动量轮作为卫星姿态控制系统的关键执行部件,对其故障检测对维持卫星的正常运行具有重要意义。首先对动量轮系统的故障进行分析,在建立动量轮线性离散状态空间模型的基础上,把动量轮的故障检测作为时变参数系统的跟踪来处理,将动量轮的模型参数作为扩展的状态空间中的状态量,使得动量轮物理模型参数与状态空间中的状态量有对应关系,通过在扩展了的状态空间上采用扩展的卡尔曼滤波,完成时变参数的跟踪。然后,将离散空间的状态量变换回连续空间中,利用物理参数与状态量的对应关系,实现对动量轮物理参数的跟踪。此方法物理意义明确,为系统的物理参数提供了定量的估计值,为进一步诊断故障原因提供了良好的基础。数值仿真表明,此方法能够通过同时检测多个故障参量,实现故障的检测并满足卫星实时性要求。     

有效性因子在卫星姿控系统集成故障诊断与容错控制中的应用

 研究了在轨卫星姿态控制系统(ACS)发生可修复性故障状况下的集成故障诊断与容错控制。考虑执行机构和敏感器分别或同时出现故障,相应地分别或同时在姿态动力学和运动学方程引入控制有效性因子和测量有效性因子,利用二级卡尔曼滤波算法求解其值,以说明系统的控制以及测量的有效程度。采用统计假设检验通过其幅值变化判断系统是否存在故障,当故障发生时,引入重构容错控制器对原控制器进行补偿控制。建立卫星闭环姿态控制系统对算法进行了仿真验证,仿真结果表明该算法快速可靠,能够满足在轨卫星姿态控制系统故障状况下的性能要求。     

Integrated active fault-tolerant control using IMM approach

An integrated fault detection, diagnosis, and reconfigurable control scheme based on interacting multiple model (IMM) approach is proposed. Fault detection and diagnosis (FDD) is carried out using an IMM estimator. An eigenstructure assignment (EA) technique is used for reconfigurable feedback control law design. To achieve steady-state tracking, reconfigurable feedforward controllers are also synthesized using input weighting approach. The developed scheme can deal with not only actuator and sensor faults, but also failures in, system components. To achieve fast and reliable fault detection, diagnosis, and controller reconfiguration, new fault diagnosis and controller reconfiguration mechanisms have been developed by a suitable combination of the information provided by the mode probabilities from the IMM algorithm and an index related to the closed-loop system performance. The proposed approach is evaluated using an aircraft example, and excellent results have been obtained     

A Diagnostic Tree Approach for Fault Cause Identification in the Attitude Control Subsystem of Satellites

Space and Earth observation programs demand stringent guarantees ensuring smooth and reliable operations of space vehicles and satellites. Due to unforeseen circumstances and naturally occurring faults, it is desired that a fault-diagnosis system be capable of detecting, isolating, identifying, or classifying faults in the system. Unfortunately, none of the existing fault-diagnosis methodologies alone can meet all the requirements of an ideal fault- diagnosis system due to the variety of fault types, their severity, and handling mechanisms. However, it is possible to overcome these shortcomings through the integration of different existing fault-diagnosis methodologies. In this paper, a novel learning-based, diagnostic-tree approach is proposed which complements and strengthens existing efficient fault detection mechanisms with an additional ability to classify different types of faults to effectively determine potential fault causes in a subsystem of a satellite. This extra capability serves as a semiautomatic diagnostic decision support aid to expert human operators at ground stations and enables them to determine fault causes and to take quick and efficient recovery/reconfiguration actions. The developed diagnosis/analysis procedure exploits a qualitative technique denoted as diagnostic tree (DX-tree) analysis as a diagnostic tool for fault cause analysis in the attitude control subsystem (ACS) of a satellite. DX-trees constructed by our proposed machine-learning-based automatic tree synthesis algorithm are demonstrated to be able to determine both known and unforeseen combinations of events leading to different fault scenarios generated through synthetic attitude control subsystem data of a satellite. Though the immediate application of our proposed approach would be at ground stations, the proposed technique has potential for being integrated with causal model-based diagnosis and recovery techniques for future autonomous space vehicle missions.     

改进故障隔离的容错联邦滤波

为了解决重置模式下联邦滤波器中子系统故障对导航系统污染的问题,提出利用故障检测函数构建时变量测噪声的容错联邦滤波结构。通过将故障子滤波器等价为量测噪声趋于无穷大的正常系统,来取代传统的故障隔离方法;推导出了子滤波器对应的最优估计值,用以消除子滤波器估计次优性对故障检测的影响;采用动态信息分配系数,以减少故障信息对全局估计的影响。采用惯性/天文/景象/地形(INS/CNS/SMNS/TERCOM)的组合导航系统进行了仿真验证,结果表明该容错联邦滤波方法在子系统发生故障时的估计性能优于故障隔离方法。因此,所提方法具有提高故障子滤波器精度、保证无故障子滤波器鲁棒性以及全局估计精度的优势,具有较高的实用价值。     

Correlation-weighted least squares residual algorithm for RAIM

The Least Squares Residual (LSR) algorithm, one of the classical Receiver Autonomous Integrity Monitoring (RAIM) algorithms for Global Navigation Satellite System (GNSS), presents a high Missed Detection Risk (MDR) for a large-slope faulty satellite and a high False Alarm Risk (FAR) for a small-slope faulty satellite. From the theoretical analysis of the high MDR and FAR cause, the optimal slope is determined, and thereby the optimal test statistic for fault detection is conceived, which can minimize the FAR with the MDR not exceeding its allowable value. To construct a test statistic approximate to the optimal one, the Correlation-Weighted LSR (CW-LSR) algorithm is proposed. The CW-LSR test statistic remains the sum of pseudorange residual squares, but the square for the most potentially faulty satellite, judged by correlation analysis between the pseudorange residual and observation error, is weighted with an optimal-slope-based factor. It does not obey the same distribution but has the same non-central parameter with the optimal test statistic. The superior performance of the CW-LSR algorithm is verified via simulation, both reducing the FAR for a small-slope faulty satellite with the MDR not exceeding its allowable value and reducing the MDR for a large-slope faulty satellite at the expense of FAR addition.     

Fault Detection and Diagnosis of Aircraft Actuators using Fuzzy-Tuning IMM Filter

This paper proposes a new interacting multiple model (IMM) filter for actuator fault detection. Since each individual filter of the IMM filter uses the combined information of the estimation values from all the operating filters, it can effectively estimate system parameter variations, thereby it can diagnose the actuator damage with an unknown magnitude. In this study, to diagnose the actuator failure fast and accurately, fuzzy logic is used to tune a transition probability among multiple models. This makes the fault detection process smooth and reduces the possibility of false fault detection. Also, a discrete fault tolerant command tracker is derived to cope with actuator damages. To validate the performance of the proposed fault detection and diagnosis (FDD) algorithm, numerical simulations are performed for a high performance aircraft system.     

飞机机轮速度检测系统故障模式及判别方法研究

本文通过机轮测速系统组成、工作原理、分析出具体故障模式,提出机轮速度检测系统故障判别方法,解决飞机防滑刹车系统外装式机轮速度检测系统速度传感器无信号输出故障故障定位问题,提出机轮测速系统安装使用注意事项,提高飞机防滑刹车系统维修性、测试性、保障性技术水平,更好满足用户需求.     

涡扇发动机传感器故障诊断的快速原型实时仿真

为快速高效地完成涡扇发动机传感器故障诊断算法的硬件在环仿真试验,构建了以NI CompactRIO为核心的传感器故障诊断系统的快速原型实时仿真平台.基于一簇卡尔曼滤波器,在LabVIEW编程环境中建立了传感器故障诊断系统.分别在涡扇发动机模型稳态和动态工作时完成了对单个传感器故障的检测、隔离和重构的硬件在环仿真试验并验证了算法精度.经过大量试验,结果表明:基于卡尔曼滤波器理论的诊断算法能在传感器故障情况下确保控制系统安全运行,诊断精度最高可达1.4%;同时表明,该快速原型实时仿真平台的设计是成功的.研究工作为发动机传感器故障诊断系统的半物理仿真试验奠定了基础.      

用于检测操纵面损伤的故障检测滤波器

针对飞行中典型的操纵面损伤故障,以故障检测滤波器为基础,开发了一种建立在离散模型上的快速、稳定的故障检测及诊断算法,可以在几个采样周期内同时精确检测出多个操纵面的损伤程度,故障检测滤波器的稳定性、收敛性也得到证明,具有较好的实用性。几种组合故障下的仿真研究证实了上述结论。     

基于深度森林的卫星ACS执行机构与传感器故障识别

针对卫星姿态控制系统（ACS）闭环回路的故障难以辨识的问题,引入深度森林算法,实现执行机构与传感器故障识别。首先针对可获取的少量卫星姿态控制系统遥测数据,结合系统动力学特性,研究合适的特征选择和特征提取方法,再结合深度森林算法进行故障信息学习与辨识,建立故障预测模型,实现执行机构故障与传感器故障的识别。半物理仿真结果表明：在存在气浮台干扰力矩、卫星转动惯量未知、飞轮非线性特性、闭环故障传播等多种不利因素情况下,深度森林算法对于执行机构和传感器故障具有高效的识别能力。     

Application of Analytic Redundancy-based Fault Diagnosis of Sensors to Onboard Maintenance System

Analytic redundancy-based fault diagnosis technique (ARFDT) is applied to onboard maintenance system (OMS). The principle of the proposed ARFDT scheme is to design a redundancy configuration using ARFDT to enhance the functions of redundancy management and built in test equipment (BITE) monitor. Redundancy configuration for dual-redundancy and analytic redundancy is proposed, in which, the fault diagnosis includes detection and isolation. In order to keep the balance between rapid diagnosis and binary hypothesis, a filter together with an elapsed time limit is designed for sequential probability ratio test (SPRT) in the process of isolation. Diagnosis results would be submitted to central maintenance computer (CMC) together with BITE information. Moreover, by adopting reconstruction, the designed method not only provides analytic redundancy to help redundancy management, but also compensates the output when both of the sensors of the same type are faulty. Our scheme is applied to an aircraft’s sensors in a simulation experiment, and the results show that the proposed filter SPRT (FSPRT) saves at least 50% of isolation time than Wald SPRT (WSPRT). Also, effectiveness, practicability and rapidity of the proposed scheme can be successfully achieved in OMS.     

航空发动机控制系统传感器FDIA系统仿真

基于卡尔曼滤波器组,建立了航空发动机控制系统传感器故障诊断系统,实现了对单个传感器故障的检测、隔离与重构(FDIA),确保了发动机控制系统即使在传感器故障发生的情况下,依然能安全可靠地工作.同时利用Simulink建立一个通用的发动机传感器故障诊断仿真平台,在此仿真平台上,对故障的诊断进行仿真验证,并分析了测量噪声对故障诊断系统性能的影响,为发动机在线传感器故障诊断系统的实现提供理论依据.      

Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros

This paper proposes a neural network-based fault diagnosis scheme to address the problem of fault isolation and estimation for the Single-Gimbal Control Moment Gyroscopes(SGCMGs) of spacecraft in a periodic orbit. To this end, a disturbance observer based on neural network is developed for active anti-disturbance, so as to improve the accuracy of fault diagnosis.The periodic disturbance on orbit can be decoupled with fault by resorting to the fitting and memory ability of neural network. Subsequ...     

检测滤波器在FADEC系统传感器FDI中的应用研究

研究将检测滤波器应用于全权限航空发动机控制系统(FADEC)的传感器故障检测和隔离(FDI).完整设计了针对于某型FADEC系统传感器的检测滤波器系统, 然后利用非线性发动机模型进行了仿真。数字仿真结果表明, 检测滤波器是十分有效的。      

基于UIO的航空发动机执行机构故障诊断

为了提高航空发动机诊断过程中对噪声干扰和模型参数变化的鲁棒性,应用UIO(Unknown Input Ob-server)理论估计发动机动态系统的工作状态,通过干扰正交投影弱化外界干扰对状态估计的影响,处理了航空发动机执行机构的故障诊断问题。对航空发动机执行机构设计一组UIO观测器,其中每个UIO用于监测估计对应执行机构的故障偏移量,计算系统输出理论估计值与发动机实际测量信号间的残差数据,分析残差队列的幅值特性,实现航空发动机执行机构系统的故障检测和隔离。某型涡扇发动机上的实验结果表明,与Kalman滤波算法相比,UIO诊断方法更能鲁棒地检测和隔离出执行机构故障。     

航天器智能诊断系统开发研究

提出了基于故障树的故障报警方法和诊断方法,提出了面向故障树的报警知识和诊断知识表示方法及相应的报警和诊断推理策略,最后,在Windows环境下,用BorlandC++实现了一个原型系统,并通过对“实践-四号”卫星能源系统故障模拟实验台的诊断验证了系统的有效性。     

基于双模型滤波算法的环控系统换热器故障诊断

 在建立飞机环控系统数学模型的基础上,提出采用双模型滤波方法进行参数估计、状态预测和故障诊断,提高飞机环控系统故障诊断的快速性和准确性。如果采用最小二乘算法,参数估计是静态的,故障诊断延迟一般较大;采用单模型扩展Kalman滤波算法,虽然能够实现动态估计,但不能同时兼顾稳态过程和过渡过程（突发故障）的参数估计,导致误差较大。为了解决上述难题,针对飞机环控系统换热器故障诊断,提出两模型滤波算法。该算法由两个滤波器组成,分别用于跟踪系统的稳态和过渡过程。由于采用了两滤波器模型分别匹配不同的系统特征,能够改善飞机环控系统不同状态下的参数估计和状态预测性能,从而提高系统故障诊断的精度和速度。仿真结果证实了该算法的有效性。     

卫星姿态时延反步容错控制

针对卫星在轨飞行过程中存在执行机构故障及带有常值干扰的控制问题,提出了一种将时延控制(TDC)与反步技术相结合的鲁棒容错控制方法。该方法在继承反步控制优点的同时,引入积分环节用于减小常值干扰引起的稳态误差;同时,利用TDC的逼近能力来补偿执行机构的故障,且对设计者而言,故障信息不需要进行在线的检测和分离,而仅需要一步状态迭代。基于Lyapunov方法从理论上证明了系统的稳定性。最后,将该方法应用于卫星的姿态调节控制,仿真结果表明该控制器能有效地抑制外部干扰、参数不确定性和执行机构故障的约束,在完成姿态调节控制的同时,具有良好的过渡过程品质。