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基于状态观测器,提出一种模型含有不确定性误差情况下传感器增益漂移和零点漂移故障的诊断与分离方法。首先讨论了状态观测器的残差信号与模型误差与传感器故障间的动态关系;然后采用逆系统求解和优化拟合等手段,提出一种估算传感器增益和零点偏移量的方法。 相似文献
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多重故障诊断方法研究* 总被引:2,自引:0,他引:2
针对复杂系统故障模式较多时难以满足实时性的要求,对传统降阶观测器的诊断方法进行了改进,提出了基于动态观测器的诊断方法。该方法通过设计一个动态观测器去检测一系列故障,其效果等同于使用了一族观测器。当把耦合故障表示成故障信号的组合时,该方法可推广到耦合故障的诊断中。 相似文献
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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。 相似文献
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基于自适应观测器的时滞系统执行器故障诊断 总被引:4,自引:0,他引:4
该文研究了一类含有未知输人干扰和模型不确定性的线性时滞系统的故障诊断问题。通过设计自适应诊断观测器,得到了一种新型的鲁棒执行器故障诊断方法。首先针对确定性系统分别设计了检测观测器和自适应诊断观测器,前者能够检测出故障的发生,后者能够理想地估计出故障随时间变化的形状。然后考虑系统的外部干扰和模型不确定,改进了自适应诊断观测器的算法,证明了故障诊断系统的稳定性,提高了故障诊断系统的鲁棒性。最后给出了故障检测过程中阈值的选取原则。仿真结果表明算法具有良好的诊断性能。 相似文献
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一种鲁棒故障检测与分离的参数化方法 总被引:1,自引:1,他引:0
针对具有未知干扰输入的多变量线性系统,提出一种鲁棒故障检测与分离的完全参数化方法。利用最近的结果,基于Luenberger未知输入观测器矩阵的特征值及一组自由参数向量,分别给出了系统干扰解耦和故障分离的充要条件。通过适当选择满足一些约束的自由参数,仅使用单一观测器实现了鲁棒故障检测与分离设计。该方法提供了所有设计自由度。一个数值例子证明了该方法的有效性。 相似文献
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Model-based fault detection technique has a broad range of applications because of the small change to the system when the system state is known to be available and the low cost. For nonlinear stochastic distribution systems containing uncertain disturbance term, a model-based fault detection and failure time prediction scheme is proposed in this paper, and observers are designed to detect whether the incipient fault has occurred in the system. The residual is obtained by comparing the output of the actual system with the output of the observer. When the residual exceeds the threshold value obtained by derivation, it is determined that the fault has occurred in the system. The fault size can then be estimated in real time and used to determine the time to failure (TTF) or the remaining useful life of the system. The TTF of the system is obtained by comparing the magnitude of the current system fault with the fault threshold. Finally, the feasibility of the presented fault detection scheme is proved by the Lyapunov stability theory and the validity of the scheme is proved by computer simulation. 相似文献
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Joachim Deutscher 《International journal of control》2016,89(3):550-563
In this article, finite-dimensional residual generators are directly designed for Riesz-spectral systems with bounded input and output operators to detect faults. This is achieved by using finite-dimensional observers, that can estimate linear functionals of the state without spillover. These observers allow for a decoupling of the unknown disturbances from the estimation error dynamics under mild assumptions. Then, a finite-dimensional residual generator is obtained by approximately decoupling the state from the residual, that is generated by the observer states and the outputs. It is shown that the resulting approximation error can be made small by increasing the observer order. Then, fault detection with the finite-dimensional residual generator can be assured by introducing a time-varying threshold. A faulty Euler–Bernoulli beam with structural damping illustrates the proposed finite-dimensional fault detection approach. 相似文献
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针对一类随机切换非线性系统的故障检测和故障估计问题,提出了一种基于交互式多模型和容积卡尔曼滤波(IMM CKF)的系统状态估计算法。该算法利用容积卡尔曼滤波(CKF)在不同时刻对每个子系统进行状态估计,把不同子系统状态估计结果融合得到最终的状态估计,实现对系统真实状态的估计。针对一类随机切换非线性系统发生执行器故障,采用IMM CKF估计系统状态;然后分析了IMM CKF算法的稳定性;根据状态估计结果,构造残差信号,设计残差评价函数,检测故障发生。当检测到故障发生时,设计增广系统,对故障幅值进行估计。通过仿真实验验证提出算法的有效性,结果表明该算法可以较为准确地诊断系统故障。 相似文献
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Sensor fault tolerant control of nonlinear Takagi–Sugeno systems. Application to vehicle lateral dynamics 下载免费PDF全文
This paper presents a new scheme for sensor fault tolerant control for nonlinear systems based on the Takagi–Sugeno modeling. First, a structured residual generator aimed at detecting and isolating sensor faults is designed. A bank of observers controlled either by only one system output or a set of outputs is then implemented, leading to a set of state estimates. The parallel distributed compensation structure is adopted to design the fault tolerant controller. The novelty in this paper is that the estimated state used in the controller is a weighted state vector obtained from all the estimated states provided by the different observers. The weighting functions depend on the residual vector signals delivered by the residual generator. They are designed to avoid crisp switches in the control law. Indeed, the interesting feature of the proposed approach is to avoid the commonly used switching strategy. For each residual component, the greater its magnitude is, the less the weight affected to the corresponding state estimate is. Consequently, the controller only uses estimations computed on the basis of healthy measurements. The closed‐loop stability is studied with the Lyapunov theory, and the obtained conditions are expressed as a set of linear matrix inequalities. The proposed residual generation and fault tolerant controller are applied to a vehicle lateral dynamics affected by sensor faults. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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针对联邦滤波器子系统同时存在硬故障和软故障问题,提出一种适用于联邦滤波结构的两级故障检测方法。首先,构造联邦结构残差2χ检验法对系统硬故障进行检测,再用第k-m步未发生故障时的全局最优估计信息构造滑动残差检验函数,对未检测出的软故障进行时间积累,进而检测软故障,同时,联邦滤波信息分配系数根据软故障检测函数进行自适应调节。通过SINS-Galileo-北斗组合导航系统仿真对比分析了基于局部滤波残差2χ检验法和本文提出的故障检测方法,结果表明:该故障检测方法对系统硬故障和软故障具有较高的故障检测灵敏度,能够提高组合导航系统的可靠性。 相似文献
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This paper proposes a novel subspace approach towards identification of optimal residual models for process fault detection and isolation (PFDI) in a multivariate continuous-time system. We formulate the problem in terms of the state space model of the continuous-time system. The motivation for such a formulation is that the fault gain matrix, which links the process faults to the state variables of the system under consideration, is always available no matter how the faults vary with time. However, in the discrete-time state space model, the fault gain matrix is only available when the faults follow some known function of time within each sampling interval. To isolate faults, the fault gain matrix is essential. We develop subspace algorithms in the continuous-time domain to directly identify the residual models from sampled noisy data without separate identification of the system matrices. Furthermore, the proposed approach can also be extended towards the identification of the system matrices if they are needed. The newly proposed approach is applied to a simulated four-tank system, where a small leak from any tank is successfully detected and isolated. To make a comparison, we also apply the discrete time residual models to the tank system for detection and isolation of leaks. It is demonstrated that the continuous-time PFDI approach is practical and has better performance than the discrete-time PFDI approach. 相似文献
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针对一类可控标准型基础上添加非线性模型误差与故障项的MIMO非线性系统,结合反推技术,提出了神经网络自适应控制方案,对模型误差与故障项进行在线估计。文中鲁棒项用于补偿逼近模型误差,当检测出系统故障时,通过调整各步骤的虚拟控制量来补偿故障项,消除故障项对系统的影响。通过理论证明实现了提出的控制方法使得各残差信号一致有界,并最终收敛到一个小的邻域内。实例仿真表明该方案的可行性。 相似文献
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Zhenhai Li 《International journal of control》2013,86(1):171-182
In this paper we consider a model-based fault detection and isolation problem for linear time-invariant dynamic systems subject to faults and disturbances. We use a state observer scheme that cancels the system dynamics and defines a residual vector signal that is sensitive only to faults and disturbances. We then design a stable fault detection and isolation filter such that the ?∞-norm of the transfer matrix function from disturbances to the residual is minimised (for fault detection) subject to the constraint that the transfer matrix function from faults to residual is equal to a pre-assigned diagonal transfer matrix (for isolation of possibly simultaneous occurring faults). Our solution is given in the form of linear matrix inequalities using state-space techniques, as well as a model matching problem using matrix factorisation techniques. A numerical example is given to illustrate the efficiency of the fault detection and isolation filter. 相似文献
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针对传感器的故障诊断与故障数据重构问题,提出一种基于改进型长短期记忆网络(LSTM)和随机森林(RF)的混合算法.首先,运用改进型LSTM算法对传感器的输出序列进行预测,将预测值与实际值作差得到残差序列.然后,通过RF算法对残差序列进行分类,识别出传感器的故障状态.当传感器诊断的结果为故障工作状态时,利用改进型LSTM的预测值重构故障数据.所提的改进LSTM-RF算法在功能上既可以对传感器故障类型进行诊断,又可以对故障数据进行重构.实验结果表明,改进的LSTM-RF算法的传感器故障识别准确率在不同的数据集上均能大于97%,故障数据重构的均方根误差小于4%;相比标准的LSTM-RF算法,改进的LSTM-RF算法在收敛速度提高的同时故障数据重构的精度提高了0.4%. 相似文献