捷联惯导可靠性的奇偶向量方法

本文用奇偶向量讨论了捷联惯导的可靠性,建立了奇偶方程,进而利用假设检验求取故障检测的决策函数和隔离函数.研究了用卡尔曼滤波方法估计动态测量等项误差,用误差估计补偿奇偶向量,从而实现用常值门限进行故障检测和故障隔离.对六个单自由度陀螺组成的捷联余度系统的数字仿真表明该方法是有效的.     

不确定系统鲁棒解析余度的最优设计

本文给出了鲁棒故障检测的性能指标，采用广义特征结构理论求解性能指标，可得出用于系统故障检测的最优和次最优鲁棒解析余度。文中针对喷气发动机在不同工作点运行的一组模型，采用本文方法对系统的故障进行了检测，仿真结果表明，对所有的工作点模型均能得到满意的检测效果。     

一种鲁棒故障检测与反馈控制的最优集成设计方法

研究线性不确定系统的反馈控制器与鲁棒故障检测滤波器集成设计问题.基于新提出 的性能指标函数,将鲁棒故障检测滤波器设计问题归结为最优化问题,通过求解Riccati方程可 得到鲁棒故障检测滤波器设计问题的最优解.在共用同一状态观测器的情况下,将反馈控制器 和鲁棒故障检测滤波器的集成设计问题归结为两目标优化问题,解决了同时满足闭环控制系统 设计要求和故障诊断系统鲁棒性能的最优集成设计问题.简例验证了提出算法的有效性.     

基于主成分分析和奇偶向量的动态检测方法

导航系统中冗余IMU传统故障检测方法由于数学模型过于复杂,计算量大,存在较大延时,难以实现实时故障检测,而主成分分析法仅仅应用于静态情况下的故障检测与隔离,针对主成分分析法无法在动态情况下对冗余IMU进行故障检测的缺点,提出了一种基于奇偶空间法改进主成分分析的故障检测算法,该方法利用奇偶向量隔离车辆的动态变量,以消除动态变量对故障检测的影响,再用PCA方法检测数据以实现对车辆传感器信息的实时检测,通过将原始数据集转置到特征平面来形成图案,实现了IMU传感器正常与故障模式的准确分离,提高了冗余IMU故障检测的结果精确性和可靠性。实验结果表明,该方法能够较好检测动态状态下冗余IMU的故障,提高了主成分分析的故障检测性能,可有效消除导航系统运动的负面影响。     

时滞离散系统最优输出跟踪控制的灵敏度法

针对具有有限时间二次型性能指标的时滞离散系统,研究了最优输出跟踪控制问题.通过引入一个灵敏度参数,将原最优输出跟踪控制问题转化为不含超前项和时滞项的一族两点边值问题.得到的最优输出跟踪控制律由状态向量的线性解析函数和伴随向量级数形式的补偿项组成,其解析函数由一次性求解R iccati矩阵差分方程和矩阵差分方程得到,补偿项由求解伴随向量差分方程的递推公式得到.仿真结果表明了该方法的有效性.     

模糊非线性奇偶方程故障诊断方法

研究基于模糊模型和奇偶(一致性)方程的非线性系统执行器故障诊断方法.讨论了全 解耦奇偶方程的产生方法,并给出了全解耦奇偶向量存在的条件.由全解耦奇偶方程产生的残 差仅对特定执行器故障敏感,而与系统状态、扰动输入和其它执行器输入无关.用T-S模糊模型 描述非线性系统,并与全解耦奇偶方程相结合得到了模糊奇偶方程,解决了奇偶方程在非线性 系统中的应用问题.将执行器故障模型用刻度因子和偏差表示,用模糊奇偶方程产生残差,从而 可以估计故障模型的参数.文章给出了某飞机非线性模型的仿真实例.     

网络化切换控制系统故障检测与优化设计

研究存在未知短时延、丢包和系统不确定性的网络化切换控制系统故障检测与时域优化问题. 首先基 于观测器构建残差发生器, 结合Lyapunov 函数方法和平均驻留时间方法分析系统的稳定性, 并以线性矩阵不等式(LMI) 形式给出故障检测滤波器的求解方法; 然后为了改善故障检测系统的性能, 采用后置滤波器对残差信号进行时域优化, 并利用奇偶空间方法给出其最优解; 最后设计并推导出自适应阈值. 仿真结果验证了所提出方法的有效性.

     

柴油机故障自动纠错方法研究与仿真

研究柴油机故障的自动纠错问题,提高纠错可靠性.针对柴油机复杂的内部结构,利用提取的待检测零件数据构建故障检测目标函数,柴油机发生故障时的复杂数据构造的目标函数具有多极值特性,传统的通过直接对目标函数训练找到最优解的神经网络纠错方法极易陷入局部极小而不能准确检测柴油机故障,导致柴油机故障自动纠错的可靠性不高.为解决上述问题,提出利用支持向量机的自动纠错方法,可提取的零件数据训练目标函数得到粗糙集特征空间,构建粗糙集最优分割超平面,在向量学习机内训练超平面找到全局最优解,避免了传统方法故障检测时陷入局部最优的情况.实验表明,改进方法能够准确完成柴油机的故障检测,保证柴油机故障自动纠错的可靠性.     

基于PSO-SVM的电机故障检测

传统智能故障检测模型中算法初始参数复杂,选取难度较大,缺乏自学习、自组织能力、泛化能力弱,极易陷入局部极小值、算法单一等缺点.组合应用智能检测算法可整合不同算法优势,避免单一算法缺点,为此,文中提出支持向量机算法与改进粒子群算法相结合的电机故障检测模型:以电机故障特征频率特征数据为基础,首先使用改进全局求解性能的粒子群算法求解影响支持向量机分类检测性能的最佳参数,然后把最佳参数应用于的擅长模式识别的支持向量机算法,进行样本数据的训练,构建故障检测模型;最后,使用故障检测模型对电机的状态进行预测.实验结果表明,采用该方法进行故障检测的准确率,比传统的神经网络方法提高17%,比纯支持向量机算法提高3.33%.     

无轨胶轮车全液压制动系统故障诊断研究

针对无轨胶轮车全液压制动系统长期工作在煤矿井下恶劣的环境中而易于发生故障的问题,提出了基于支持向量机的故障诊断方法。应用支持向量机回归估计算法建立全液压制动系统的故障预测辨识模型,将支持向量机模式分类算法应用于故障分离,并在Matlab环境下分别进行了故障检测与故障分离试验。试验结果表明,将支持向量机方法应用于无轨胶轮车全液压制动系统故障诊断中是完全可行的,可以有效提高故障检测效率和诊断准确率。     

Optimal design of robust analytical redundancy for a redundant strapdown inertial navigation system

In this paper, a new method of calculating the parity vector is proposed. A performance criterion for robust fault detection is given. By using the theory of generalized eigenstructure, the criterion can be solved to obtain the parity. The new method was used for the failure detection and isolation of a redundant strapdown inertial navigation system. By comparison with the classical Potter method, the new parity is more sensitive to failures, giving rise to the conclusion that the optimal design of robust analytical redundancy is efficient and practical.     

一种双探头纸张定量传感器故障诊断方法研究

介绍了一种双探头纸张定量传感器的原理及性能，给出了基于卡尔曼滤波器的传感器故障诊断算法，将其用于造纸过程纸张量传感器，实现了对传感器探头部分部件故障的探明和隔离，从而使传感器的可靠性得以提高，仿真实验研究结果表明，文中所给出的故障诊断方法及能准确地探明和隔离出传感器连续性和间断性故障。     

Robust Isolation Of Sensor Failures

Sensor self‐validity check is a critical step in system control and fault diagnostics. In this paper, a robust approach to isolate sensor failures is proposed. First, a residual model for a given system is built off‐line and directly based on input‐output measurement data. The residual model outputs are called “primary residuals” and are zero when there is no fault. Most conventional approaches to residual model generation are indirect, as they first require the determination of state‐space or other models using standard system identification algorithms. Second, a new max‐min design of structured residuals, which can maximize the sensitivity of structured residuals with respect to sensor failures, is proposed. Based on the structured residuals, one can then isolate the sensor failures. This design can also be done in an off‐line manner. It is an optimization procedure that avoids local optimal solutions. Simulation and experimental results demonstrated the effectiveness of the proposed method.     

Robust Fault Detection Using Subspace Aided Data Driven Design

The paper deals with the design of robust fault detection system using subspace aided data driven techniques. Because of unavailability of system matrices for the complex processes, a new algorithm has been proposed to identify a robust parity vector directly from the process data. The identified parity vector is used to construct a residual generator having robustness against the process and sensor noises and increased sensitivity to actuator and sensor faults. The performance of the proposed fault detection scheme has been analyzed by simulation studies on a coupled liquid three tank system (CLTS).     

Optimal decision f usion given sens or rules

When all the rules of sensor decision are known ,the optimal distributed decision fusion ,which relies only on the joint conditional probability densities , can be derived for very general decision systems. They include those systems with interdependent sensor observations and any network structure. It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman- Pearson criterion. Local decision rules of a sensor with communication from other sensors that are optimal for the sensor itself are also presented ,which take the form of a generalized likelihood ratio test . Numerical examples are given to reveal some interesting phenomena that communication between sensors can improve performance of a senor decision ,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.     

Robust fault detection and isolation in stochastic systems

This article outlines the formulation of a robust fault detection and isolation (FDI) scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves estimating sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the proposed robust FDI system.     

Optimal decision fusion given sensor rules

When all the rules of sensor decision are known,the optimal distributed decision fusion,which relies only on the joint conditional probability densities, can be derived for very general decision systems. They include those systems with interdependent sensor observations and any network structure. It is also valid for m-ary Bayesian decision problems and binary problems under the Neyman-Pearson criterion. Local decision rules of a sensor withfrom other sensors that are optimal for the sensor itself are also presented, which take the form of a generalized likelihood ratio test. Numerical examples are given to reveal some interesting phenomem that communication between sensors can improve performance of a senor decision,but cannot guarantee to improve the global fusion performance when sensor rules were given before fusing.     

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。     

Optimal sensor rules and unified fusion rules for multisensor multi-hypothesis network decision systems with channel errors

In this paper, for general jointly distributed sensor observations, we present optimal sensor rules with channel errors for a given fusion rule. Then, the unified fusion rules problem for multisensor multi-hypothesis network decision systems with channel errors is studied as an extension of our previous results for ideal channels, i.e., people only need to optimize sensor rules under the proposed unified fusion rules to achieve global optimal decision performance. More significantly, the unified fusion rules do not depend on distributions of sensor observations, decision criterion, and the characteristics of fading channels. Finally, several numerical examples support the above analytic results and show some interesting phenomena which can not be seen in ideal channel case.