采样数据系统最优诊断观测器设计

研究采样数据(sampled-data,SD)系统最优诊断观测器的直接设计方法.首先从传递函数角度分析一般离散时间系统诊断观测器的Luenberger条件,由此构造出SD诊断观测器,并推导出其混杂动态关系.然后将SD故障检测问题定义为一个特定的比值型优化问题,并应用互内外分解法和代数方法构制其最优诊断观测器,使得产生的离散时间残差对连续时间未知输入具有鲁棒性,但对故障却是敏感的.仿真结果验证了所提设计方法的有效性.     

基于广义回归神经网络的传感器故障诊断研究

针对诊断传感器偏置故障与漂移故障的难点问题,提出了一种基于广义回归神经网络(GRNN)的传感器故障诊断方法。该方法充分利用控制系统闭环回路测控信息,建立一组多输入单输出GRNN观测器,通过将观测器输出与传感器实际输出相比较获取残差序列,获得基于残差序列的传感器偏置故障和漂移故障的辨识策略,实现控制系统传感器故障在线诊断。仿真结果表明:该方法可以快速准确地检测和分离传感器故障,辨识传感器故障类型、故障大小以及故障发生的时间。     

基于动态观测器零极点优化的网络控制系统故障检测

针对网络化控制系统中在有限频扰动和噪声下的传感器故障检测问题,构建一种新型的观测器结构:动态观测器.利用动态观测器零点可配置这一新特性,提出零点和极点联合优化配置的故障检测方法.所提出的动态观测器是在传统比例积分观测器的基础上扩展而成,更具一般性,具有零点可配置的特点,能够克服传统观测器零点是固定的限制.将动态观测器的零点配置到有限频率测量扰动信号和网络传输延时等外部扰动的特征频谱处,利用零点的特性更好地衰减扰动对残差的影响.给出观测器故障检测的综合性能指标,结合$H_2/H_\infty$优化方法优化动态观测器极点.最后以网络化两轮自平衡机器人的模型为例,比较动态观测器、比例积分观测器、比例观测器的故障检测效果.仿真结果验证了所提动态观测器故障检测方法的优势和有效性.     

一种基于滑模—神经网络观测器的故障检测和诊断方法

本文针对一类非线性系统，提出了一种用于故障检测和诊断的滑模观测器方法．其 中，观测器中的滑模项保证了该系统在无故障情况时的鲁棒性，并且系统运行的滑动区域提供了故障检测的条件．当检测出故障之后，观测器中的故障估计部分被启动，利用RBF神经网络估计故障，从而能在线辨识故障的形态．仿真结果验证了该方法的有效性．     

基于模糊神经网络的非线性系统故障诊断

研究故障观测器优化设计,针对一类非线性动态系统,在考虑系统的输入输出包含外部扰动及建模误差等不确定性项[1]的情况下,为了提高所设计观测器对系统数学模型的在线跟踪能力从而进一步提高故障诊断的鲁棒性减少系统的误报警率,提出了基于模糊神经网络的诊断方法。利用神经网络以及模糊系统对非线性函数的无限逼近能力,设计了基于T-S模糊模型[2]的神经网络自适应观测器来拟合系统的非线性模型和系统的非线性故障特性。由Lyapunov稳定性方法获得调整观测器权重的规律。对所用改进方法的收敛性进行了证明,并通过仿真实例说明了诊断方法的有效性和使用性。     

基于鲁棒观测器的带间隙三明治系统故障预报

工程实践中常见的带间隙的三明治系统的准确故障预报具有重要的现实意义,为此,本文构建了一种新的动态鲁棒观测器对其进行故障预报.首先,通过将非光滑项转化为干扰项的方法,将间隙非光滑三明治系统转化为可用动态鲁棒观测器设计方法设计的系统.其次,采用零点配置和最小化基准区间观测器的范数(H_∞,F/H_,F)指标的方法确定动态鲁棒观测器的增益矩阵.最后,通过仿真,分别比较了基于非光滑鲁棒观测器和基于传统观测器的故障预报效果,比较结果表明:鲁棒观测器能够及时地准确预报传统观测器无法预报的故障,且有效减少了故障的漏报和错报现象.     

不确定动态系统的执行器故障检测与重构

讨论不确定动态系统的执行器故障检测与重构问题.以滑模观测器为基础,利用状态和输出变换方法与奇异值分解方法相结合对系统进行降阶,提出一种鲁棒故障重构观测器.给出了优化滑模策略,并作了严格论证,保证对系统不确定性具有鲁棒性和跟踪系统状态的收敛性.应用等价输出控制的概念和设计的鲁棒故障重构观测器,获取故障信息并实现执行器故障的检测与重构.最后通过数值仿真验证了该方法的有效性和可靠性.     

一类基于神经网络非线性观测器的鲁棒故障检测和诊断

利用神经网络的非线性建模能力，对一类具有建模不确定项的非线性系统提出一种基于观测器的故障检测和诊断的方法。设计的观测器不仅能实现故障检测，而旦应用神经网络设计的故障估计器能在线估计系统中的故障向量。通过分析验证了该方法对系统中的建模误差和外部扰动具有良好的鲁棒性。仿真结果表明所提出的方法是有效的。     

基于滑模观测器和神经网络的传感器故障诊断方法比较研究

针对航空发动机传感器故障诊断中各种方法的优势和劣势,选择滑模观测器和神经网络这两种故障诊断方法分别对航空发动机转速传感器进行故障诊断研究,采用实验室搭建的发动机实验台DGEN380的实验数据,选择对航空发动机控制系统影响较大的偏置故障、漂移故障、脉冲故障、周期性干扰故障这四类传感器故障进行诊断。研究结果表明,滑模观测器和IPSO-BP神经网络都能实现航空发动机传感器的故障诊断;滑模观测器方法可以诊断出偏置故障、脉冲故障和周期性干扰故障,但不能诊断出传感器发生的漂移故障;IPSO-BP神经网络方法可以诊断出偏置故障、漂移故障、脉冲故障和周期性干扰故障。因此,滑模观测器在故障诊断中可能会出现漏诊的现象,IPSO-BP神经网络相对滑模观测器而言不会出现漏诊的现象。     

基于滑模观测器的机器人系统传感器故障诊断

本文基于变结构滑模状态观测器，提出了一种在模型含有不确定性误差的情况下，机器人控制系统传感器故障的鲁棒诊断方法．通过滑模观测器与传感器的输出构造残差信号，然后从残差中提取故障信息，实现故障的检测与分离．实验结果验证了该方法的有效性．     

基于最优解耦的鲁棒故障检测方法研究

系统地研究了线性系统在模型参数变动和存在外部扰动下的控制系统故障的鲁棒故障检测方法。分析了在与干扰信号不能完全解耦的情况下,如何设计残差信号使其对模型参数变化及噪声不敏感,而对故障敏感,由此得出了对未知输入具有鲁棒性的条件方程式,并基于最优解耦理论,通过矩阵的奇异值分解实现了最优解耦。在给出的仿真实例中,验证了该文提出方法的有效性。     

Sliding mode observers for detection and reconstruction of sensor faults

This paper proposes two methods for detecting and reconstructing sensor faults using sliding mode observers. In both methods, fictitious systems are introduced in which the original sensor fault appears as an actuator fault. The original sensor faults are then reconstructed using a ‘secondary’ sliding mode observer. For both methods, there are certain conditions which must be satisfied for successful fault detection and reconstruction. The methods are demonstrated using a chemical process example.     

A bounded-error approach to simultaneous state and actuator fault estimation for a class of nonlinear systems

This paper proposes an approach for the joint state and fault estimation for a class of uncertain nonlinear systems with simultaneous unknown input and actuator faults. This is achieved by designing an unknown input observer combined with a set-membership estimation in the presence of disturbances and measurement noise. The observer is designed using quadratic boundedness approach that is used to overbound the estimation error. Sufficient conditions for the existence and stability of the proposed state and actuator fault estimator are expressed in the form of linear matrix inequalities (LMIs). Simulation results for a quadruple-tank system show the effectiveness of the proposed approach.     

An integrated trade-off design of observer based fault detection systems

In this paper, problems related to the integrated design of observer based fault detection systems are addressed. In the context of norm based residual evaluation, a trade-off between false alarm rate (FAR) and fault detection rate (FDR) is first formulated. Aiming at minimizing FAR under given FDR, an approach is then presented for the design of observer based fault detection systems. For this purpose, the well-established factorization technique is applied. Finally, study is devoted to the comparison between the existing methods and the proposed one.     

Robust sensor faults detection for induction motor using observer

Current sensor is one of the key elements in the control system of induction motor.Whether the accurate measurement of variables reflecting motor operation status can be made will directly affect the control effect on motor system and therefore the timely,accurate detection of sensor fault is necessary.This paper brings forward an observerbased method of residual generation and fault detection on the basis of the mathematical model of the induction motor.As whether or not the nonlinear part satisfies the Lipschitz conditions does not limit the observer design,the application of such an observer is expanded.Meanwhile,the contradiction between robust error and fault sensitivity is also settled.The correctness and effectiveness of such method are verified by experimental testing on the simulated fault which also casts light on engineering practice.     

航空发动机鲁棒残差设计故障诊断研究

针对航空发动机的故障,设计了基于观测器的鲁棒故障诊断器。介绍了故障诊断中残差生成的一般形式和干扰解耦的基本原理。针对建模中存在的模型不确定性问题,在假定干扰分布已知的前提下,设计了鲁棒残差生成器,实现了残差对干扰解耦的目标,最后仿真模拟了燃油计量活门和尾喷管作动筒故障的情形。仿真结果表明,所设计的鲁棒残差生成器产生的残差具有对干扰低敏感而对故障高敏感的特性,实现了残差对干扰解耦的目标。     

Detecting and diagnosing faults in dynamic stochastic distributions using a rational B-splines approximation to output PDFs

This paper presents a novel approach to detect and diagnose faults in the dynamic part of a class of stochastic systems . the Such a group of systems are subjected to a set of crisp inputs but the outputs considered are the measurable probability density functions (PDFs) of the system output, rather than the system output alone. A new approximation model is developed for the output probability density functions so that the dynamic part of the system is decoupled from the output probability density functions. A nonlinear adaptive observer is constructed to detect and diagnose the fault in the dynamic part of the system. Conver-gency analysis is performed for the error dynamics raised from the fault detection and diagnosis phase and an applicability study on the detection and diagnosis of the unexpected changes in the 2D grammage distributions in a paper forming process is included.     

故障检测滤波器的鲁棒性设计

该文设计了全维非线性未知输入观测器，实现检测滤波器对于被控对象模型误差和系统不确定性因素的干扰解耦。某型航空发动机控制系统传感器和执行机构的故障检测仿真实验表明，具有干扰解耦的故障检测滤波器可以准确地对传感器和执行机构的故障进行检测和隔离，提高了检测滤波器对于故障检测的鲁棒性。     

Model-based real-time thermal fault diagnosis of Lithium-ion batteries

Ensuring safety and reliability is a critical objective of advanced Battery Management Systems (BMSs) for Li-ion batteries. In order to achieve this objective, advanced BMS must implement diagnostic algorithms that are capable of diagnosing several battery faults. One set of such critical faults in Li-ion batteries are thermal faults which can be potentially catastrophic. In this paper, a diagnostic algorithm is presented that diagnoses thermal faults in Lithium-ion batteries. The algorithm is based on a two-state thermal model describing the dynamics of the surface and the core temperature of a battery cell. The residual signals for fault detection are generated by nonlinear observers with measured surface temperature and a reconstructed core temperature feedback. Furthermore, an adaptive threshold generator is designed to suppress the effect of modelling uncertainties. The residuals are then compared with these adaptive thresholds to evaluate the occurrence of faults. Simulation and experimental studies are presented to illustrate the effectiveness of the proposed scheme.     

基于RBF神经网络观测器飞控系统故障诊断

为了解决非线性系统采用解析方法进行故障诊断困难的问题,利用神经网络可逼近任意连续有界非线性函数的能力,提出了一种基于RBF神经网络观测器的故障检测与诊断方法,并详细论述了该故障诊断方法的构造原理。以含有非线性项的飞行控制系统的作动器模型为例,仅作动器的输入输出可测量,通过构造RBF神经网络观测器来拟合作动器系统模型,逼近其在正常情况下的输出。最后在飞控系统的闭环控制环境下,对作动器的三种典型故障进行了计算机仿真诊断,结果表明故障诊断方法是有效的。