基于自适应观测器的四旋翼无人飞行器传感器故障诊断方法

针对四旋翼无人飞行器传感器故障诊断问题,提出一种用于四旋翼无人飞行器加速度计和陀螺仪故障同时发生的故障检测与隔离以及故障偏差值估计的非线性诊断方法.首先,在建立飞行器动力学模型和传感器模型的基础上,构建四旋翼无人飞行器传感器故障检测与诊断系统.其次,利用故障观测器完成传感器故障的检测与隔离,基于Laypunov方法设计非线性自适应观测器对未知故障偏差值进行估计.最后,在传感器测量噪声存在的情况下,证明自适应律的稳定性和参数收敛性.实验结果表明,该方法能有效进行传感器的故障检测与隔离,实现对传感器故障偏差的估计与跟踪.     

基于自适应观测器的四旋翼无人飞行器多传感器故障诊断方法

为了实现对四旋翼无人飞行器多传感器故障检测与诊断,提出一种基于自适应观测器的多传感器故障诊断方法。首先,在建立飞行器动力学模型和传感器模型的基础上,将传感器故障视为虚拟执行器故障,构建四旋翼无人飞行器多传感器故障检测与诊断系统;其次,设计非线性观测器实现多故障检测和与隔离,基于Laypunov方法设计非线性自适应观测器实现对多故障偏差值的估计;最后,在传感器测量噪声存在的情况下,证明自适应律的稳定性和参数收敛性。实验结果表明,该方法能有效进行多传感器的故障检测与隔离,实现对多传感器故障偏差的同时估计与跟踪。     

A hybrid fault diagnosis approach using neural networks

A hybrid fault diagnosis method is proposed in this paper which is based on the parity equations and neural networks. Analytical redundancy is employed by using parity equations. Neural networks then are used to maximise the signal- to- noise ratio of the residual and to isolate different faults. Effectiveness of the method is demonstrated by applying it to fault detection and isolation for a hydraulic test rig. Real data simulation shows that the sensitivity of the residual to the faults is maximised, whilst that to the unknown input is minimised. The simulated faults are successfully isolated by a bank of neural nets.     

基于特征相关性的牵引系统主回路接地故障诊断

本文针对目前机车、动车牵引系统中主回路接地故障的精确定位问题, 提出了一种基于特征相关性的故障诊断方法. 该方法通过在线计算与故障关联的特征变量, 提取相关故障特征指标, 并考虑各故障特征指标间的相关性, 利用典型相关分析得到残差, 以实现快速故障检测. 进一步, 构建基于残差方向的故障隔离方法, 实现准确地故障定位. 现场实验表明, 与传统基于相关性的故障诊断方法以及实际工程应用方法相比, 在存在较大测量噪声与暂态工况变化时, 本文所提方法能实现更好的故障检测与隔离性能, 具有良好的应用价值.     

On the design of fault detection filters with game‐theoretic‐optimal sensitivity

The problem of residual generation for fault detection and isolation in the presence of measurement noise for a class of nonlinear systems is considered. Exploiting the tools and the results of the geometric approach to the problem of residual generation, a method is proposed for the design of a filter which attenuates the effect of the measurement noise on the residual in the case in which the effect of the fault on the residual is minimal. Copyright © 2002 John Wiley & Sons, Ltd.     

A robust detection and isolation scheme for abrupt and incipientfaults in nonlinear systems

This paper presents a robust fault diagnosis scheme for abrupt and incipient faults in nonlinear uncertain dynamic systems. A detection and approximation estimator is used for online health monitoring. Once a fault is detected, a bank of isolation estimators is activated for the purpose of fault isolation. A key design issue of the proposed fault isolation scheme is the adaptive residual threshold associated with each isolation estimator. A fault that has occurred can be isolated if the residual associated with the matched isolation estimator remains below its corresponding adaptive threshold, whereas at least one of the components of the residuals associated with all the other estimators exceeds its threshold at some finite time. Based on the class of nonlinear uncertain systems under consideration, an isolation decision scheme is devised and fault isolability conditions are given, characterizing the class of nonlinear faults that are isolable by the robust fault isolation scheme. The nonconservativeness of the fault isolability conditions is illustrated by deriving a subclass of nonlinear systems and of faults for which these conditions are also necessary for fault isolability. Moreover, the analysis of the proposed fault isolation scheme provides rigorous analytical results concerning the fault isolation time. Two simulation examples are given to show the effectiveness of the fault diagnosis methodology     

一类基于神经网络非线性观测器的鲁棒故障检测

针对一类仿射非线性动态系统,提出了一种基 于神经网络非线性观测器的鲁棒故障检测与隔离的新方法．该方法采用神经网络逼近观测器 系统中的非线性项,提高了状态估计的精度,并从理论上证明了状态估计误差稳定且渐近收 敛到零;另一方面引入神经网络分类器进行故障的模式识别,通过在神经网络输入端加入噪 声项来进行训练,提高神经网络的泛化逼近能力,从而保证对被监测系统的建模误差和外部 扰动具有良好的鲁棒性．最后,利用本文方法针对某型歼击机结构故障进行仿真验证,仿真 结果表明本文方法是有效的．     

基于神经网络的非线性观测器及在线故障检测

提出一种基于径向基函数神经网络的非线性观测器的设计方法，并将其应用于复杂非线性系统的故障检测与隔离。该方法将神经网络离线学习与在线学习相结合，获取系统输入输出的非线性动力学特性，进而实时计算出残差并进行逻辑判决，可显著提高故障检测的快速性、鲁棒性及准确率。最后，针对非线性同步交流电机的结构损伤故障进行了仿真，结果表明本文所提方法的有效性。     

A pulsed plasma thruster fault detection and isolation strategy for formation flying of satellites

The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for pulsed plasma thrusters (PPTs) that are employed in the attitude control subsystem (ACS) of satellites tasked to perform formation flying (FF) missions. A hierarchical methodology is proposed that consists of three fault detection and isolation (FDI) approaches, namely (i) a “low-level” FDI scheme, (ii) a “high-level” FDI scheme, and (iii) an “integrated” FDI scheme. Based on the data from the electrical circuit of the PPTs, the proposed “low-level” FDI scheme can detect and isolate faults in the PPT actuators with a good level of accuracy, however the precision level is poor and below expectations with the misclassification rates as expressed by False Healthy and False Faulty parameters being too high. The proposed “high-level” FDI scheme utilizes data from the relative attitudes of the FF mission. This scheme has good detection capabilities, however its isolation capabilities are not adequate. Finally, the proposed “integrated” FDI scheme takes advantage of the strengths of each of the above two schemes while reducing their individual weaknesses. The results demonstrate a high level of accuracy (99.79%) and precision (99.94%) with a misclassification rates that are quite negligible (less than 1%). Furthermore, the proposed “integrated” FDI scheme provides additional and interesting information related to the effects of faults in the thrust production levels that would not have been available from simply using the low or the high level schemes alone.     

A geometric approach to nonlinear fault detection and isolation in a hybrid three-cell converter

In this paper, the problem of fault detection and isolation in a three-cell converter is investigated using a nonlinear geometric approach. This powerful method based on the unobservability distribution is used to detect and isolate the faulty cell in the three-cell converter. First, a model describing the faults in the cells is presented. The geometric approach is then applied on this faulty model to generate residual signals based on a sliding-mode observer that allows the detection of faults in the three-cell converter. Numerical results show the effectiveness of the proposed sliding-mode residual generators for fault detection and isolation in the three-cell converter.     

Actuator and Sensor Fault Detection and Failure Prediction for Systems with Multi-dimensional Nonlinear Partial Differential Equations

This paper presents a new model-based fault detection and failure prediction framework for a class of multi-input and multi-output (MIMO) nonlinear distributed parameter systems (DPS) described by partial differential equations (PDE) with actuator and sensor faults. The fault functions cover both abrupt and incipient faults. A Luenberger type observer is used to monitor the health of the DPS as a detection observer on the basis of the nonlinear PDE representation of the system and by utilizing only the measured output vector. By taking the difference between measured and estimated outputs, a residual signal is generated for fault detection. If the detection residual exceeds a predefined threshold, a fault is claimed to be active. Once an actuator or a sensor fault is detected, an appropriate fault parameter update law is developed to learn the fault dynamics online with the help of an additional measurement. Later, an explicit formula is introduced to estimate the time-to-failure in the presence of an actuator/sensor fault by utilizing the limiting values of the output vector along with the estimated fault parameter vector. Eventually, the effectiveness of the proposed detection and prediction framework is demonstrated on a nonlinear process.

     

不确定飞行控制系统中断故障检测与分离

研究不确定飞行控制系统执行器中断故障检测与分离问题,同时设计了状态反馈控制器和检测器,在保证闭环控制系统稳定的前提下,通过设计的检测器对系统状态进行重组以产生残差进而检测执行器的中断故障。此外,通过设计一组分离器,可以确定出执行器发生故障的位置。最后,通过研究一个飞行控制系统模型验证了所提出方法的有效性。     

Neuro-fuzzy identification applied to fault detection in nonlinear systems

This article describes a fault detection method, based on the parity equations approach, to be applied to nonlinear systems. The input–output nonlinear model of the plant, used in the method, has been obtained by a neural fuzzy inference architecture and its learning algorithm. The proposed method is able to detect small abrupt faults, even in systems with unknown nonlinearities. This method has been applied to a real industrial pilot plant, and good performance has been obtained for the experimental case of fault detection in the level sensor of a level control process in the said industrial pilot plant.     

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.     

Robust identification and fault diagnosis based on uncertain multiple input–multiple output linear parameter varying parity equations and zonotopes

We present a robust fault diagnosis method for uncertain multiple input–multiple output (MIMO) linear parameter varying (LPV) parity equations. The fault detection methodology is based on checking whether measurements are inside the prediction bounds provided by the uncertain MIMO LPV parity equations. The proposed approach takes into account existing couplings between the different measured outputs. Modelling and prediction uncertainty bounds are computed using zonotopes. Also proposed is an identification algorithm that estimates model parameters and their uncertainty such that all measured data free of faults will be inside the predicted bounds. The fault isolation and estimation algorithm is based on the use of residual fault sensitivity. Finally, two case studies (one based on a water distribution network and the other on a four-tank system) illustrate the effectiveness of the proposed approach.     

基于神经网络观测器的飞行器传感器故障检测

为了避免传感器故障对飞控系统的影响,实现传感器故障的快速检测与隔离,提出了一种基于神经网络观测器(NNOB)的传感器故障检测方法。在建立四旋翼飞行器姿态故障模型的基础上,利用非线性观测器得到的期望输出和传感器测量值设计基于神经网络(NN)的传感器故障观测器,利用扩展卡尔曼滤波器(EKF)更新神经网络的权值参数,通过Lyapunov理论证明权值参数更新的收敛性,最终构建出一种基于神经网络观测器的传感器故障检测系统。数值仿真实验结果表明,与现有神经网络故障检测方法相比,所提方法具有更高的故障检出率与更好的跟踪性能。     

Real-Time Model-Based Fault Detection and Isolation for UGVs

The paper presents a model-based sensor fault detection and isolation system applied in real-time to unmanned ground vehicles. Structural analysis is applied on the nonlinear model of the vehicle for building the residual generation module, followed by an ad-hoc residual evaluation module for detecting single and multiple sensor faults. The overall proposed diagnosis scheme has been tested in real-time on a real mobile robot in an outdoors environment and for different tasks. The obtained experimental results are satisfactory in terms of diagnosis performance and real-time implementation.     

Fault Detection Based On Online Probability Density Function Estimation

This study presents a new fault detection scheme based on the probability density function (PDF) of system output. Unlike the classical fault detection and diagnosis methods, in the proposed method, distribution of the system output is estimated online. To achieve this goal, an algorithm is introduced to estimate PDF online using fuzzy logic. Furthermore, convergence of this algorithm is investigated. Then, a residual is constructed that can show the existence of a fault in the system. The main advantages of the proposed method are robustness against measurement noise, even though it does not need the exact model and measured data of inputs and states. Simulation results show that this scheme can detect abrupt faults very well.     

Design of unknown input functional observers for nonlinear systems with application to fault diagnosis

This paper considers the design of low-order unknown input functional observers for robust fault detection and isolation of a class of nonlinear Lipschitz systems subject to unknown inputs. The proposed functional observers can be used to generate residual signals to detect and isolate actuator faults. By using the generalized inverse approach, the effect of the unknown inputs can be decoupled completely from the residual signals. Conditions for the existence and stability of reduced-order unknown input functional observer are derived. A design procedure for the generation of residual signals to detect and isolate actuator faults is presented using the proposed unknown-input observer-based approach. A numerical example is given to illustrate the proposed fault diagnosis scheme in nonlinear systems subject to unknown inputs.     

Actuator and sensor fault detection and isolation for nonlinear systems subject to uncertainty

This work addresses the problem of simultaneous actuator and sensor fault detection and isolation (FDI) for control affine nonlinear uncertain systems in the absence of measurement noise. The FDI is achieved by using a bank of filters, which utilize a subset of the measurements along with prescribed values of the control actuators to estimate states and compute expected process behavior. Residuals are next defined as the difference between the observed and expected behavior. Detectability conditions are developed, which, upon satisfaction, ensure that each residual remains sensitive to a subset of fault scenarios in the presence of uncertainty. To this end, first the ability of observers in providing bounded estimation error for a generalized class of nonlinear uncertain systems is rigorously established. These bounds allow determining thresholds that account for the impact of uncertainty on each residual. Finally, the ability of the proposed framework to achieve FDI by ensuring a unique residual breaching pattern for each fault scenario is established. The efficacy of the FDI framework subject to uncertainty and measurement noise is illustrated using a chemical reactor example.