一种基于EMD的模拟电路故障特征提取方法

为克服小波分析中混频现象对模拟电路故障特征提取造成的不准确,提出了一种基于经验模式分解的模拟电路故障特征提取方法。该方法通过对模拟电路输出信号进行EMD得到若干个内禀模态函数,以各IMF的能量作为故障判别的特征。并针对EMD中的端点效应问题,提出了基于遗传优化的最小二乘支持向量机预测方法。仿真实验结果证明了该方法的有效性。     

改进的强跟踪飞机舵面快速故障诊断方法

针对多模型自适应估计(multiple model adaptive estimation, MMAE)方法适应突变故障能力差、多重渐消因子强跟踪算法滤波发散、故障条件概率计算量大等问题,提出一种改进的多重渐消因子强跟踪多模型自适应估计(strong tracking multiple model adaptive estimation, STMMAE)快速故障诊断方法。通过多重渐消因子提高了故障突变时滤波器的跟踪性能;通过改进一步预测协方差阵更新方程,保证了滤波器稳定性,提高了估计精度;采用基于欧几里得范数的飞机舵面故障概率快速计算方法,降低了故障概率计算量。对比仿真表明,该算法跟踪性强、速度快、精度高,具有较好的鲁棒性和稳定性。     

基于改进型经验模分解的陀螺漂移趋势提取

非线性,非平稳的时间序列经过经验模分解,可以得到一组内模函数和一个基本的趋势项。本文针对陀螺弱非线性、非平稳漂移时间序列的趋势项提取这一问题,引入了改进的经验模分解方法。该方法通过二拍自适应滤波对原经验模分解中局部均值的估计方法进行了改进,并利用自适应边界估值来抑制其边缘效应。给出了应用该方法的具体步骤,并对一组陀螺漂移序列进行了实际处理。实验结果表明,该方法有效提取了陀螺的漂移趋势项。     

Design and simulation of fault diagnosis based on NUIO/LMI for satellite attitude control systems

This paper presents a scheme of fault diagnosis for flexible satellites during orbit maneuver. The main contribution of the paper is related to the design of the nonlinear input observer which can avoid false alarm arising from the disturbance from orbit control force. The effects of orbit control force on the fault diagnosis system for satellite attitude control systems, including the disturbing torque caused by the misalignments and the model uncertainty caused by the fuel consumed, are discussed, where standard Luenberger observer cannot work well. Then the nonlinear unknown input observer is proposed to decouple faults from disturbance. Besides, a linear matrix inequality approach is adopted to reduce the effect of nonlinear part and model uncertainties on the observer. The numerical and semi-physical simulation demonstrates the effectiveness of the proposed observer for the fault diagnosis system of the satellite during orbit maneuver.     

基于稳健MM估计的REKF RAIM算法

基于鲁棒扩展卡尔曼滤波(robust extended Kalman filter, REKF)的接收机自主完好性监测(receiver autonomous integrity monitoring, RAIM)对双星故障模式的检测及识别效果相对较差,尤其当故障矢量具有较高的空间一致性时, M估计的稳健性会受到极大破坏。针对这一问题,提出基于稳健MM估计的REKF RAIM算法, MM估计是兼具高崩溃污染率和高估计效率的两步抗差估计方法,首先采用具有高崩溃污染率的最小截断二乘(least trimmed squares, LTS)估计获得稳健性高的迭代初值和尺度参数,然后采用IGG III方案得到最终的参数估计值,并设计一种基于特征斜率的快速选星方法降低LTS估计的计算量。仿真结果表明,相比于基于M估计的REKF, MMREKF在双星故障模式下具备更高的稳健性,对双星故障模式有更好的检测与识别能力。     

An Improved Sensor Fault Diagnosis Scheme Based on TA-LSSVM and ECOC-SVM

Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and fault identification. Firstly, trend analysis combined with least squares support vector machine (TA-LSSVM) method is proposed and implemented to detect faults. Secondly, an improved error correcting output coding-support vector machine (ECOC-SVM) based fault identification method is proposed to distinguish different sensor failure modes. To demonstrate the effectiveness of the proposed scheme, experiments are conducted with an MTi-series sensor, and some comparisons are made with other fault identification methods. The experimental results demonstrate that the proposed fault diagnosis scheme offers an essential improvement with detection real-time property and better identification accuracy.     

Novel robust fault diagnosis method for flight control systems

A novel robust fault diagnosis scheme, which possesses fault estimate capability as well as fault diagnosis property, is proposed. The scheme is developed based on a suitable combination of the adaptive multiple model （AMM） and unknown input observer （UIO）. The main idea of the proposed scheme stems from the fact that the actuator Lock-in-Place fault is unknown （when and where the actuator gets locked are unknown）, and multiple models are used to describe different fault scenarios, then a bank of unknown input observers are designed to implement the disturbance de-coupling. According to Lyapunov theory, proof of the robustness of the newly developed scheme in the presence of faults and disturbances is derived. Numerical simulation results on an aircraft example show satisfactory performance of the proposed algorithm.     

基于未知输入滤波器的过驱动系统故障诊断方法

过驱动系统由于执行器间的冗余性导致故障往往具有严重的耦合性,从而给故障的分离带来很大困难。针对过驱动系统的执行器乘性故障,提出了一种基于未知输入滤波器的故障诊断方法。首先,提出了一种新的未知输入滤波器结构,设计方便。然后,将指定执行器的故障看作未知输入,通过一组结构化残差来实现故障分离,并在此基础上,提出了一种故障估计方法。最后,通过ADMIRE飞行器的近似线性化模型仿真验证了所提方法的有效性。     

基于EMD与关联维数的故障诊断AR模型

提出了一种基于EMD和关联维数的转子系统故障诊断方法。该方法用EMD将转子系统振动信号分解成若干个基本模式分量IMF,对包含主要故障信息的IMF分量建立AR模型,AR模型自回归参数的关联维数作为特征量神经网络识别转子系统的工作状态和故障类型。对实验数据分析的结果表明,该方法能有效地应用于转子系统的故障诊断。     

基于置信规则库的并发故障诊断方法

针对具有不确定性的并发故障诊断问题,提出基于属性权重和权衡分析的置信规则库(belief rule base, BRB)诊断方法。以属性权重大小来表示属性与特定故障模式之间的相关性,设计能够反映故障模型约束的基于差分进化的优化算法,通过相邻故障模式置信度与预设阈值的权衡分析完成对并发故障诊断。该方法仅需构造单个置信规则库来有效处理各种不确定性信息,与已有研究方法相比极大地降低了建模复杂度。诊断结果不仅能得到故障的并发情况,还可分辨故障的主次关系,并且建模和推理过程开放,可解释性强。最后以船用柴油机的并发故障诊断作为实例,验证了所提方法能够有效的诊断出并发故障并且模型具有较好的稳定性。     

基于偏差分离原理的卫星执行机构故障诊断

提出一种同时估计输入时滞和控制输入故障的方法。首先,基于偏差分离的思想,分别建立含有输入时滞和控制输入故障的一般控制系统与卫星姿态控制系统的数学模型。其次,基于二阶Kalman滤波对控制输入故障以及输入时滞进行估计。最后,对所提出的方法进行了数学仿真验证,特别地,基于“快舟一号”卫星控制系统半物理仿真平台进行了半物理仿真验证,证明了方法的可行性和有效性。     

Iterative learning based fault detection and estimation in nonlinear systems

Aiming at a class of nonlinear systems that contains faults,a novel iterative learning scheme is applied to fault detection,and a novel algorithm of fault detection and estimation is proposed.This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator,and then uses the residuals and the differencevalue signal of the adjacent two residuals to gradually revise the introduced virtual faults,which can cause the virtual faults to close to the practical faults in systems,thereby achieving the goal of fault detection for systems.This algorithm not only makes full use of the existing valid information of systems and has a faster tracking convergent speed than the proportional-type(P-type) algorithm,but also calculates more simply than the proportional-derivative-type(PD-type) algorithm and avoids the unstable effects of differential operations in the system.The final simulation results prove the validity of the proposed algorithm.     

混杂系统粒子滤波混合状态估计及故障诊断算法

混杂系统同时包含连续动态特性和离散动态特性,并且两种动态相互作用,使其故障诊断变得更加困难。针对此问题,提出了一种混合系统粒子滤波混合状态估计及故障诊断算法,提高了现有方法的适用范围和诊断效率。针对混杂系统受控迁移、自治迁移和随机迁移等特点,首先利用随机混杂自动机对系统离散状态(包括故障)和连续状态进行统一建模,重点对现有基于扩展卡尔曼粒子滤波的连续估计算法进行改进,支持利用在线监测数据来估计混杂系统各类迁移产生的各种离散和连续状态,最后根据离散状态估计结果快速实现故障诊断。通过对典型非线性混杂系统的故障诊断,证明了该方法的有效性。     

一类非线性系统的多故障检测方法研究

针对一类非线性系统,提出了一种基于最优滤波的多故障检测观测器设计方法。引入神经网络逼近系统非线性部分,由Lyapunov稳定性理论设计神经网络权值调整规则.采用对等空间方法设计最优滤波矩阵滤除干扰故障噪声,从而对目标故障进行检测和跟踪。仿真结果验证了该方法的正确性和可行性。     

基于自适应未知输入观测器的多故障快速重构

针对一类非线性函数中耦合执行器故障的非线性动态系统, 提出一种基于自适应未知输入观测器的多故障快速重构方法, 通过引入比例项提高故障重构的快速性。首先, 将执行器故障进行解耦处理并构建包含传感器故障的增广系统。然后, 综合H_∞性能指标给出状态估计误差的稳定性证明。接着, 将观测器增益矩阵的求解转化为受线线矩阵不等式约束的非线性优化问题, 并实现执行器故障和传感器故障的多故障重构。最后, 结合单关节柔性机器人算例仿真验证了所提方法的有效性。     

汽车轮缸压力传感器故障重构方法研究与仿真

针对汽车液压制动系统中轮缸压力传感器的故障重构问题,建立了汽车液压制动系统的轮缸压力非线性模型。在此基础上,给出了一种基于滑模观测器的轮缸压力传感器故障重构方法,并将滑模观测器的设计问题转化为LMI求解问题。最后对轮缸压力传感器断路故障进行了仿真研究,讨论了不同增益对故障估计的影响。

Abstract：

A fault reconstruction scheme based on the sliding mode observer was proposed for the fault reconstruction of the wheel cylinder pressure sensor in the vehicle hydraulic braking system, as well as a nonlinearity model of the wheel cylinder in the hydraulic braking system. Furthermore, the design of the sliding-mode observer was converted to the solving of the LMI. In conclusion, the wheel cylinder pressure sensor open circuit fault simulation experiment and the influence on fault estimation over the different gain were proposed.     

旋转条件下非连续卫星导航信号处理技术

针对传统卫星导航技术无法估计旋转载体的转速, 且旋转导致的卫星信号非连续性使得定位和转速估计更加困难的问题，提出一种基于多天线结构的开环跟踪与差分卡尔曼滤波相结合的跟踪定位方法, 并利用相邻天线间的多普勒频率差值实现载体转速估计。同时，利用开环结构无反馈回路和快拍方式工作的特点实现对非连续信号的跟踪。差分卡尔曼滤波以相邻历元间差分值进行建模, 消除原始观测量中公共偏差, 实现跟踪信息的融合滤波和定位。最后，利用相邻天线间共视卫星的多普勒频率差值实现载体转速估计。基于模拟信号和实际信号的实验结果表明，所提算法能够实现对卫星信号的持续跟踪, 并且获得高精度的定位结果, 对转速的估计误差的标准差不超过1 rps。     

Observer-based fault detection scheme for a class of discrete time-delay systems

In this contribution, robust fault detection problems for discrete time-delay systems with l2-norm bounded unknown inputs are studied. The basic idea of our study is first to introduce a state-memoryless observer-based fault detection filter (FDF) as the residual generator and then to formulate such a FDF design problem as an Hen optimization problem in the sense of increasing the sensitivity of residual to the faults, while simultaneously enhancing the robustness of residual to unknown input as well as plant input. The main results consist of the formulation of such a residual generation optimization problem, solvability conditions and the derivation of an analytic solution. The residual evaluation problem is also considered, which includes the determination of residual evaluation function and threshold. A numerical example is used to demonstrate the proposed fault detection scheme.     

基于SVM和EMD 包络谱的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的调制特征和传统包络分析法的缺陷以及现实中难以获得大量典型故障样本的实际情况,提出了一种基于支持向量机(Support Vector Machine,简称SVM)和经验模态分解(Empirical Mode Decomposition,简称EMD)包络谱的滚动轴承故障诊断方法.该方法首先对原始信号进行经验模态分解,将其分解为多个固有模态函数(Intrinsic Mode Function,简称IMF)之和,然后求出包含主要故障信息的若干个IMF分量的包络谱,最后定义包络谱中各种故障特征频率处的幅值比为特征幅值比,将其作为故障特征向量,并以此作为SVM分类器的输入参数来区分滚动轴承的工作状态和故障类型.实验分析结果表明了该方法的有效性.     

基于新型学习观测器的卫星执行机构故障重构

针对在轨微小卫星出现执行机构故障的情况,提出了一种基于非线性学习观测器(nonlinear learning observer, NLO)的卫星姿控执行机构故障重构方法。文中结合迭代学习算法和递推学习算法,设计了一种新型自适应学习算法,该算法应用前一时刻和当前时刻的姿态敏感器测量输出误差在线更新故障重构信号,使得所提NLO在估计卫星姿态角速度和姿态角的同时,能够快速精确在线重构卫星姿控执行机构故障。进一步给出了所提NLO的稳定性条件,并结合线性矩阵不等式技术给出了NLO增益矩阵的详细设计方法。最后,将所提方法应用于微小卫星姿控推力器故障重构,仿真结果验证了所提方法的有效性。