飞行控制系统故障诊断与重构技术研究

对于高空长航时无人机高性能要求,提出一种多等级余度飞行控制系统;结合飞行控制系统各组成部分的结构特点,给出了传感器信号的余度信号管理技术,机载计算机通道故障诊断与仲裁技术;伺服子系统故障隔离和控制分配重构技术;试验结果表明该飞行控制系统故障诊断与重构方案设计合理可行,不仅较好地完成了飞行控制系统的余度管理任务,而且有效提高了故障检测率抑制了虚警,保证了系统的可靠性与容错能力.     

容错飞控系统中关键技术设计及工程实现

容错技术能够提高飞行控制系统的可靠性及安全性,介绍飞行控制系统三大组成部分(机载计算机、传感器、伺服机构)的容错技术;描述机载计算机的余度的架构、通道同步及交叉链路传输技术、故障检测、仲裁技术,双余度传感器数据选取及伺服机构重构容错;给出了关键技术的硬件电路与软件流程,经过大量的测试及半实物仿真试验,结果表明该飞行控制系统容错方案设计合理可行,在满足航电系统方案的前提下有效地保证了控制系统的可靠性与容错能力.     

基于鲁棒自适应观测器的飞控系统传感器故障隔离与重构

考虑到飞行控制系统闭环反馈及实时的特性,设计了多个鲁棒自适应观测器用于传感器的故障隔离与重构.这样有效的抑制了噪声和模型不确定性,以及闭环传感器输出之间的相互影响.同时对残差进行时序概率比检验,避免了故障误报和漏报.应用某型战斗机地形跟随控制系统进行仿真验证,在保证闭环反馈系统稳定的前提下,实现了传感器的在线故障隔离与重构,达到了预期的效果.     

飞控系统传感器故障诊断的在线方法研究

传感器是飞行控制系统当中的一个重要组成部分,在系统中往往利用传感器的各个输出来建立飞机的动态状态;因此,实时准确的对传感器进行故障检测和识别可有效地提高系统的安全可靠性;提出一种带有可变遗忘因子的BP神经网络在线递推学习算法,应用改进的算法对飞行控制系统的传感器故障进行实时在线的检测和识别,且利用神经网络的输出对系统进行重构;仿真结果表明提出的方法可准确的对传感器的故障进行故障诊断和容错控制.     

导弹控制系统的容错控制研究

为了提高导弹姿态控制系统的可靠性，防止由于传感器失效而引起导弹自毁，提出基于信号重构的容错控制方法．为了对姿态控制系统中的传感器进行信号重构，首先考虑弹体弹性振动的影响，分析了弹体弹性振动与姿态角之间的关系；然后分析了传感器输出信号之间的关系；最后，通过解析冗余方法，用正常工作的传感器输出信号重构出现故障的传感器的正常信号．仿真实验表明了该方法的有效性．     

考虑传感器故障的导弹姿态控制系统主动容错控制研究

针对导弹姿态控制系统惯性传感器故障,提出了基于信号重构的主动容错控制方法.分别利用基于梯形算法的数值积分器和有限时间收敛微分器对姿态角信号和角速率信号进行重构,当在线诊断出姿态角或角速率传感器故障时,以重构信号代替故障信号进行反馈控制来实现系统的主动容错控制.在建立导弹姿态控制系统模型并采用次最优控制方法设计输出反馈控制器的基础上,对所设计的主动容错控制方法进行仿真,仿真结果表明该方法是有效的.     

基于信号重构的可重构机械臂主动分散容错控制

针对可重构机械臂系统传感器故障,提出一种基于信号重构的主动分散容错控制方法. 基于可重构机械臂系统模块化属性,采用自适应模糊分散控制系统实现正常工作模式时模块关节的轨迹跟踪控制. 当在线检测出位置或速度传感器故障时,分别采用数值积分器或微分跟踪器重构相应信号,并以之代替故障信号进行反馈实现系统的主动容错控制. 此方法充分利用了冗余信息,避免了故障关节控制性能的下降对其他关节的影响. 数值仿真结果验证了所提出容错控制方法的有效性.     

三余度飞控系统余度管理算法设计与实现

利用余度技术可以很好提高飞行控制计算机系统的可靠性和容错能力;余度设计的的故障容错能力主要是通过系统的余度管理来实现的;在对余度管理算法进行了深入研究的前提下,结合嵌入式实时操作系统VxWorks的特点,详细描述了三余度飞控计算机系统余度管理算法的设计流程,采用这样的设计使得系统结构紧凑,大大提高了系统的实时性和安全性要求.     

基于SOPC的微小型无人机容错飞行控制平台设计

针对微小型无人机的高可靠性和微型化要求,将可编程片上系统(SOPC)技术和容错技术相结合,设计出一套双余度容错飞行控制平台;从容错方案的选取到硬件和软件的实现给出了具体实施方案,并对同步技术、故障诊断与定位、动态重构与隔离等关键技术进行了研究;经过仿真验证和试验表明,该方案设计合理,系统平台不仅可以实现无人机飞行控制的基本功能,而且具有很高的集成度和灵活性,并且满足一次故障安全。     

飞控系统余度管理算法在VxWorks中的实现

利用余度技术可以大大提高飞行控制计算机系统的可靠性和容错能力,余度设计的关键技术就是余度管理策略和方法,系统的故障容错能力主要是通过系统的余度管理来实现的。在有效的余度管理算法研究的前提下,结合嵌入式实时操作系统VxWorks的特点,详细描述了二余度飞控计算机系统余度管理算法在VxWorks中的调度策略。给出了设计流程和操作过程。采用这样的设计使得系统结构紧凑,提高了系统的实时性和安全性要求。     

An analytical fault diagnosis method for yaw estimation of quadrotors

A new analytical fault diagnosis method is presented for the yaw and yaw rate estimation of a quadrotor. In the existing methods, the faults of the inertial sensors and the measurement sensors are treated separately. The presented method can deal with the faults of both the z-axis gyro and the magnetic sensor by introducing the torque model, which is used as a virtual sensor. The filter design, fault detection, isolation and recovery strategies are discussed. Real flight data is used to validate the proposed approach, showing that both the z-axis gyro fault and the magnetic sensor fault can be detected.     

Fault diagnosis for satellite sensors and actuators using nonlinear geometric approach and adaptive observers

This paper presents a novel scheme for diagnosis of faults affecting sensors that measure the satellite attitude, body angular velocity, flywheel spin rates, and defects in control torques from reaction wheel motors. The proposed methodology uses adaptive observers to provide fault estimates that aid detection, isolation, and estimation of possible actuator and sensor faults. The adaptive observers do not need a priori information about fault internal models. A nonlinear geometric approach is used to avoid that aerodynamic disturbance torques have unwanted influence on the fault estimates. An augmented high‐fidelity spacecraft model is exploited during design and validation to replicate faults. This simulation model includes disturbance torques as experienced in low Earth orbits. This paper includes an analysis to assess robustness properties of the method with respect to parameter uncertainties and disturbances. The results document the efficacy of the suggested methodology.     

Robust Nonlinear Analytic Redundancy for Fault Detection and Isolation in Mobile Robot

A robust nonlinear analytical redundancy (RNLAR) technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch (MPM) and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors (PRV), which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are then transformed into a set of structured residual vectors (SRV) for fault isolation. Experimental results on a Pioneer 3-DX mobile robot are presented to justify the effectiveness of the RNLAR scheme.     

A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.     

Rainfall–runoff multi-modelling for sensor fault diagnosis

In view of the limitations of flood and pollution prevention in urban sewage networks, the control of hydraulic equipment now calls for more reliable measurements provided by different sensors. The sensor fault detection and isolation described here requires the availability of a rainfall–runoff relationship in order to apply analytical redundancy-based diagnostic procedures. However, because this relationship is conspicuously non-linear and time varying, the latter relationship is identified by using a multi-model approach. The proposed modelling approach has been successfully tested on a watershed located in an urban area of Nancy, in eastern France, using actual rainfall and runoff data taken from the sewerage control centre database. The model obtained is then used to increase the degree of information redundancy in order to implement a sensor fault diagnostic procedure. Since no statistical hypothesis on measurement uncertainties can be made, interval arithmetic is used to derive residual tolerance.     

An eigenstructure assignment approach to FDI for the industrial actuator benchmark test

This paper examines the robustness in modelling uncertainties of an observer-based fault-detection and isolation scheme applied to the industrial actuator benchmark problem. A linear dynamic model is used, and the observer is designed using eigenstructure assignment. Fault detection is achieved with a fixed threshold. The design and test example is an electromechanical system, subject to an actuator fault, a sensor fault and a load disturbance. The paper outlines the design, application, and an evaluation of the robustness of the method.     

Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy

One of the most critical issues when deploying wireless sensor networks for long-term structural health monitoring (SHM) is the correct and reliable operation of sensors. Sensor faults may reduce the quality of monitoring and, if remaining undetected, might cause significant economic loss due to inaccurate or missing sensor data required for structural assessment and life-cycle management of the monitored structure. This paper presents a fully decentralized approach towards autonomous sensor fault detection and isolation in wireless SHM systems. Instead of physically installing multiple redundant sensors in the monitored structure (“physical redundancy”), which would involve substantial penalties in cost and maintainability, the information inherent in the SHM system is used for fault detection and isolation (“analytical redundancy”). Unlike traditional centralized approaches, the analytical redundancy approach is implemented distributively: Partial models of the wireless SHM system, implemented in terms of artificial neural networks in an object-oriented fashion, are embedded into the wireless sensor nodes deployed for monitoring. In this paper, the design and the prototype implementation of a wireless SHM system capable of autonomously detecting and isolating various types of sensor faults are shown. In laboratory experiments, the prototype SHM system is validated by injecting faults into the wireless sensor nodes while being deployed on a test structure. The paper concludes with a discussion of the results and an outlook on possible future research directions.     

Development of smart sensors system for machine fault diagnosis

Machine fault diagnosis is a traditional maintenance problem. In the past, the maintenance using tradition sensors is money-cost, which limits wide application in industry. To develop a cost-effective maintenance technique, this paper presents a novel research using smart sensor systems for machine fault diagnosis. In this paper, a smart sensors system is developed which acquires three types of signals involving vibration, current, and flux from induction motors. And then, support vector machine, linear discriminant analysis, k-nearest neighbors, and random forests algorithm are employed as classifiers for fault diagnosis. The parameters of these classifiers are optimized by using cross-validation method. The experimental results show that smart sensor system has the similar performance for applying in intelligent machine fault diagnosis with reduced product cost. Developed smart sensors have feasibility to apply for intelligent fault diagnosis.     

Fault diagnosis and monitoring of oscillatory failure case in aircraft inertial system

A method for oscillatory fault detection and isolation is presented and used to detect oscillatory failures of redundant aircraft sensors involved in the computation of flight control laws. The objective is to switch off the erroneous sensor and to compute a consolidated parameter using data from valid sensors, in order to eliminate any anomaly before propagation in the control loop. The benefit of the presented method is to improve the consolidation process with a fault detection and isolation approach when only few sources (less than three) are valid. Different techniques are compared to accurately detect any behavioral change of the sensor outputs. The approach is validated on a normalized real flight data set.