基于集员未知输入观测器的风力机桨距执行器故障诊断

风力机一般放置在恶劣的环境中,其桨距执行器极易出现故障。文中针对一类含有未知但有界干扰和噪声的风力机系统的桨距执行器故障问题,设计了集员未知输入观测器对桨距的执行器故障进行检测并分离。采用气动机理和现代辨识原理建立风力机系统模型,通过优化未知输入观测器设计对系统中的干扰解耦,基于中心对称多胞体估计不考虑故障时残差的区间包络,并将其作为残差估计的上下动态阈值,实现状态估计。在上述基础上提出了利用一组集员未知输入观测器进行故障诊断的策略。仿真结果表明,在实验过程中,文中所设计的集员未知输入观测器准确地诊断出了风力机桨距执行器的3阶和5阶线性系统在发生突变故障和缓慢时变故障的时间和位置,证明了所提故障诊断策略的有效性。     

基于中心对称多胞体的四旋翼无人机执行器故障检测

本文针对四旋翼无人机姿态控制系统执行器发生的故障,设计未知输入观测器并基于中心对称多胞体实现故障检测.首先,利用泰勒展开对无人机非线性系统模型进行离散化处理,将执行器故障项视为干扰项,利用未知输入观测器来进行干扰解耦;其次,采用中心对称多胞体计算检测的残差阈值,通过比较四旋翼无人机系统产生的执行器故障残差与阈值区间完成故障检测;最后,基于MATLAB仿真平台,验证了所提出的无人机姿态控制执行器系统的故障检测的有效性.     

非方广义线性系统的观测器设计

基于含未知输入的非方广义线性系统,研究了该系统具有环路复现特性(LTR)的干扰解耦观测器设计问题.通过矩阵的初等变换和广义逆变换,将非方广义系统等价地转化为正常状态空间的广义线性系统,并运用有关定理给出观测器具有干扰解耦和环路复现特性(LTR)的充要条件,经分析可知,系统具有环路复现特性的充要条件是系统对于任何未知输入干扰解耦,则具有环路复现特性的干扰解耦观测器的设计问题转化为系统关于任何扰动都是解耦的.进一步由广义Sylvester矩阵方程的显式通解出发,指出了观测器的参数化设计方法,同时,给出了观测器的设计算法.运用Matlab进行数字仿真分析,证明该方法是有效的.     

以观测器为基础的量化网络化系统故障检测

文中针对具有未知输入和不确定扰动信号的系统,研究一类以观测器为基础的量化网络化系统故障检测问题。首先,引入时变量化器,对输出信号进行量化处理,在此基础上,对原系统建立故障检测滤波器,通过比较原系统与观测器的输出,建立故障检测滤波器误差系统。最后,基于H∞稳定性理论,将故障检测滤波器问题转化为问题。在论证过程中结合LMI有关定理,给出仿真实例,验证文中方法的有效性。     

一种基于信号相关分析的模拟电路故障诊断方法

模拟电子电路集成度日益提高,使得某些电路内部节点存在不可及性,针对这种电路,提出了一种基于信号相关分析的模拟电路故障诊断方法.将伪随机噪声信号作为模拟电路的测试激励信号,利用电路正常情况下与电路故障状态下输出响应之间的残差信号作为故障特征,通过信号相关分析实现对电路的故障检测与故障定位.仿真实验分析证明该方法操作简单,有较好的在线诊断能力,适用于诊断电路中的硬故障和软故障,能够达到较高的故障覆盖率.     

离散时间Markov跳变系统的降维观测器设计

研究一类具有未知输入的线性离散时间马尔可夫跳变系统的降维观测器设计问题.首先,针对含有未知输入的线性系统设计一种新的降维观测器,通过构造观测器增益矩阵,使得未知输入完全解耦.此外,通过线性矩阵不等式形式给出观测器存在的充分条件,并证明观测器误差在有限时间意义上是稳定的,保证了估计的良好暂态性.最后,通过实例验证了所提方法的有效性和可行性.     

基于电流残差的逆变器故障诊断方法研究

针对三电平逆变器中不同故障类型输出波形相似的情况,提出一种基于电流残差的逆变器故障诊断方法。该方法运用混合逻辑动态模型和三电平仿真模型经过一系列的数学运算提取线电流残差,作为逆变器故障诊断的一部分输入特征;取逆变器桥臂上的电压信号作为故障诊断的特征信号,利用小波变换提取故障信号作为另一部分输入特征;将两部分特征向量进行有效组合,构建新的且能够区分所有故障模式的特征向量,最后利用BP神经网络进行逆变器的故障诊断。仿真结果表明,该方法数据处理量小,诊断精度高,具有较强的可行性和有效性。     

基于降维观测器的Takagi-Sugeno模糊非线性系统的故障诊断

针对以往的研究在实现执行器故障诊断时需要故障或故障导数以及干扰上界已知的不足,设计一种降维观测器来实现非线性动态系统中执行器故障的诊断。首先,利用Takagi-Sugeno模糊模型对非线性动态系统进行建模,通过坐标变换将系统中的执行器故障分离出来。然后,采用线性矩阵不等式与Lyapunov泛函分别设计H∞输出反馈控制器与降维观测器,通过设计的降维观测器得到了Takagi-Sugeno模糊非线性系统中执行器故障的渐近估计。最后,通过仿真分析验证了所提方法是有效的。     

基于RBF神经网络的控制系统传感器故障诊断方法

针对现行研究中压铸机实时检测与控制系统中相关传感器的常见故障问题,通过对人工神经网络理论与方法的学习,建立了一种基于径向量基函数神经网络RBFNN的控制系统传感器故障诊断观测器模型.通过来自压铸机的实测参数进行模型训练,采用模糊K均值聚类算法选取聚类中心,利用该观测器确定传感器输出值与传感器实际输出值之间的残差,以此判断传感器是否发生故障.仿真结果表明,RBFNN观测器具有较强的非线性处理和任意函数逼近的能力,预测精度高,学习时间短,网络运算速度快,性能稳定,可满足传感器故障诊断的要求.     

基于FPGA的传感器故障诊断算法研究与实现

对于目前传感器设备故障诊断方法在诊断过程中会导致实际生产中弱故障诊断不灵敏,无法有效识别故障,并且充分考虑实际生产过程中的特点,提出基于FPGA的传感器故障诊断算法设计。在不同残差空间中分析故障,使系统对于弱故障诊断的精准性得到提高。使用迭代算法对主元分析算法模型进行更新,利用残差空间平方加权预测误差变量的重构对故障进行确定,从而对传感器故障进行在线诊断。最后,通过企业实际数据实现此方法的在线故障诊断实验。实验结果表明,此种方法能够有效诊断传感器设备故障。     

Incipient Fault Detection Using an Associated Adaptive and Sliding-Mode Observer for Quadrotor Helicopter Attitude Control Systems

An associated adaptive and sliding-mode observer (AASMO) design is proposed to detect and estimate the incipient actuator faults of a quadrotor. The incipient faults considered are physical structure aging and quadrotor leakage. First, disturbances and nonlinear parameters are considered in system formulation for a realistic mathematical model of the quadrotor. Its fault model is also introduced. Second, the decomposed subsystems are obtained through coordinate transformations to separate the incipient faults from the disturbances. For the subsystem with no disturbance, the adaptive observer can estimate the incipient faults. For the subsystem with disturbances, the sliding-mode observer has strong robustness against the disturbances. Dynamic error convergence and system stability can also be guaranteed by Lyapunov stability theory. Finally, the simulation results of quadrotor helicopter attitude systems validate the efficiency of the proposed AASMO-based incipient fault detection algorithm.     

多传感器故障检测与隔离的残差产生器设计方法研究

为了解决多传感器故障检测与隔离这一难题,建立了包含卡死、增益时变和偏差时变等三种典型传感器故障的数学模型.借助输出方程,将待检测的传感器故障转换到系统状态方程中进行处理.设计出既能检测出故障,又能将各个故障进行有效隔离的残差产生器,同时还要给出设计残差产生器过程中未知参数的求解方法.用算例对上述设计结果的有效性进行计算...     

Hybrid fault diagnosis for telerobotics system

Fault detection and isolation (FDI) of a class of networked control systems (NCS), applied for telerobotics system is studied in this paper. The considered NCS application is related to telerobotics system, where it is modelled with a hybrid manner, by including the continuous, discrete, uncertain, and stochastic aspects of all the system components. The main considered components of the NCS namely the network system and controlled system are completely decoupled according to their operation characteristics. The network part is taken as a discrete and stochastic system in presence of non-structured uncertainties and external faults, while the controlled part is considered as a continuous system in presence of input and output faults. Two model based fault diagnosis approaches are proposed in this paper. The first concerns a discrete and stochastic observer applied to the network system in order to detect and isolate system faults in presence of induced delay on the network part. The second is based on the analytical redundancy relations (ARR) allows detecting and isolating the input and output system’ faults. Experimental results applied on telerobotics system, show the performance and the limit of the proposed fault diagnosis approach.     

Robust Fault Estimation and Accommodation for a Class of T–S Fuzzy Systems with Local Nonlinear Models

This paper focuses on the problems of fault estimation and accommodation for a class of T–S fuzzy systems with local nonlinear models and having an external disturbance and sensor and actuator faults, simultaneously. A fuzzy robust fault estimation observer is designed to estimate the system state and sensor and actuator faults. Compared with existing results, the observer not only is robust to the disturbance but also has a wider application range and more freedom for design. To compensate for the effect of faults and to stabilize the closed-loop system, an observer-based fault-tolerant controller is proposed. The separate design of the observer and controller avoids coupling between them. Finally, a simulation is conducted to demonstrate the effectiveness of the proposed method.     

State and Disturbance Estimator for Time-Delay Systems With Application to Fault Estimation and Signal Compensation

For a state-space time-delay system with linearly coupled input and output disturbances, a simultaneous state and disturbance estimation technique is developed. For a nonlinear state-space time-delay system with dependent input and output disturbances, a nonlinear estimator is also proposed to estimate system state and disturbance at the same time. The proposed estimator techniques are applied next to estimate system state and fault signal. Via actuator and/or sensor signal compensation, a simple and efficient fault-tolerant operation can be realized. In the developed design, no limitations and prior knowledge are required on the considered faults. Moreover, identical actuator and/or sensor switches and control gain reconstruction are not necessary. Therefore, the proposed estimation and fault-tolerant scheme is economical and convenient in practical applications. After that, the design techniques are extended to the case of systems with a class of uncoupled input and output faults. Examples and simulations given show excellent signal estimation and fault-tolerant performance.     

High-Gain Observers With Sliding Mode for State and Unknown Input Estimations

To handle the state estimation of a nonlinear system perturbed by a scalar disturbance distributed by a known nonlinear vector, a sliding-mode term is incorporated into the nonlinear high-gain observer (HGO) to realize a robust HGO. By imposing a structural assumption on the unknown input distribution vector, the observability of the disturbance with respect to the output is safeguarded, and the disturbance can be estimated from the sliding surface. Under a Lipschitz condition for the nonlinear part, the nonlinear observers are designed under the structural assumption that the system is observable with respect to any input. In the sliding mode, the disturbance under an equivalent control becomes an increment of Lipschitzian function, and the convergence of the estimation error dynamics can be proven similar to the analysis of HGOs. The proposed technique can be applied for fault detection and isolation. The simulation results for the bioreactor application demonstrate the effectiveness of the proposed method.      

Data-driven design of robust fault detection system for wind turbines

In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct final decision. The effectiveness of the proposed approach is finally illustrated by simulations on the wind turbine benchmark model.