参数估计法检测与诊断铁路运输车减振装置故障

本文利用参数估计法检测与诊断铁路运输车减振装置故障,并分别对6自由度和4自由度模型进行了仿真和模拟实验。     

一类非线性系统参数偏差型故障的实时检测与诊断

本文针对一类较一般的多变量非线性时变随机系统,给出了一种全新的用于其参数偏差型故障的实时检测与诊断方法。这类故障包容了诸如部件参数的阶跃型和缓变型故障等,所给仿真实例证实了本文方法的有效性。     

一类非线性系统参数突变故障的检测与诊断

本文针对一类非线性系统提出了一种非线性自适应故障检测滤波器,可以实时检测并分离、估计出其参数的突变故障。仿真实例说明了本文方法的有效性。     

故障检测与诊断技术

本文介绍了故障检测与诊断技术的历史及发展状况,对其所用的各种方法分类进行了详细评述,指出了当前急待解决的若干前沿课题,并对这门技术的发展进行了展望。     

工业机器人故障的实时检测与诊断

本文首先建立了直流电机驱动机器人的驱动系统的动态故障模型,它包容了电枢电阻、电感等故障.随后建立了工业机器人传感器的动态故障模型,可包容位置、速度及力传感器的阶跃型和缓变型故障.在此基础上,应用作者提出的可处理一类非线性系统参数偏差型故障的系统性算法,成功地检测并诊断出这些故障.最后进行了数值仿真验证.     

基于STM32故障电弧检测装置的设计

对负载电流信号的检测是判断低压配电线路中是否发生电弧故障的有效方法之一。用ADC模块对负载电流信号进行采样,将发生电弧故障时的电流波形与正常工作时的电流波形相比较,检测是否有电弧故障发生。比较的电流波形特征量主要有过零点后的上升率、电流正半周期采样时间宽度和正半周期采样幅值,将这三个特征量作为电弧故障识别算法的判断条件,检测电弧故障。为了在硬件上验证该时域检测算法的可行性和有效性,将电弧故障检测算法移植到STM32平台,设计出了一台基于STM32的故障电弧检测装置样机。该样机可以实现电流信号采集、过零点检测、数据处理以及串联电弧故障检测识别。在以日光灯、开关电源和吸尘器为屏蔽负载的实验结果表明,该装置可以检测出串联电弧故障,且可靠性高,不会在没有产生故障电弧的情况下产生误判。     

血流动力学检测装置转换器故障自动诊断方法

在医院血液科的护理中,需要实时进行血流动力学检测装置转换器故障分析,为了提高血流动力学检测装置转换器故障自动诊断能力,提出一种基于频谱特征分析的血流动力学检测装置转换器故障自动诊断方法。构建血流动力学检测装置转换器故障信号采集模型,对采集的血流动力学输出压力特征、脉动特征以血流冲量等特征量进行联合特征分析,结合关联谱分析方法进行血流动力学检测装置转换器故障信号各频率分量检测识别,实现血流动力学检测装置转换器故障信号的频谱特征分析,根据频谱差异性进行故障辨识和诊断。仿真结果表明,采用该方法进行血流动力学检测装置转换器故障诊断的准确性较高,自动化水平较好。     

控制系统执行机构故障的检测与诊断

评述了近年来执行机构故障检测与诊断的主要研究成果和发展现状,并给出了各种诊断方法的典型应用,对各种诊断方法的优缺点进行了比较,最后指出了该领域有待进一步研究的若干问题和发展趋势.     

Adaptive divided difference filtering for simultaneous state and parameter estimation

A novel adaptive version of the divided difference filter (DDF) applicable to non-linear systems with a linear output equation is presented in this work. In order to make the filter robust to modeling errors, upper bounds on the state covariance matrix are derived. The parameters of this upper bound are then estimated using a combination of offline tuning and online optimization with a linear matrix inequality (LMI) constraint, which ensures that the predicted output error covariance is larger than the observed output error covariance. The resulting sub-optimal, high-gain filter is applied to the problem of joint state and parameter estimation. Simulation results demonstrate the superior performance of the proposed filter as compared to the standard DDF.     

State and parameter estimation in chemical and biochemical processes: a tutorial

This paper aims at giving an overview of available results of state and parameter approaches for chemical and biochemical processes. It is largely organized as a tutorial and starts with a brief reminder concerning the design of extended Luenberger (ELO) and Kalman (EKO) observers, followed by an illustrative nonlinear observer algorithm. Evaluation of the performance of classical observers in presence of model uncertainties will serve as a basis for the motivation of designing asymptotic and interval observers, that do not require the knowledge of the process kinetics. The design of state observers with known kinetic models but uncertain kinetic parameters will then be considered via suggestions of improvements of the EKO and the introduction of two other types of observers (observers where the unknown parameters are used as design parameters; adaptive observers). Finally, the design of on-line parameter estimation schemes will be introduced. One of the objectives of the present survey is also to suggest new research directions.     

多故障的奇偶方程-参数估计诊断方法

提出一种将奇偶方程与参数估计相结合的多故障诊断方法。构造了一种新的奇俩方程,其产生的残差仅对一个传感器故障和一个执行器故障敏感。将传感器和执行器故障模型表示成刘度因子和偏差的形式,应用卡尔曼滤没方法对各故障模型参数进行估计。某型号飞机控制系统的仿真结果表明,新方法能对传感器故障和执行器故障同时存在的线性系统进行诊断,有效地估计出各故障的模型参数。     

Analysis of the variability of joint input-output estimation methods

It has been recently established that, when estimating parametric models on the basis of closed loop data, the frequency domain variability of direct and various indirect methods may significantly differ from one another. This paper continues this work by analysing the performance of certain common joint input-output estimation methods.     

Sparse estimation from noisy observations of an overdetermined linear system

This note studies a method for the estimation of a finite number of unknown parameters from linear equations, which are perturbed by Gaussian noise. In the case the unknown parameters have only few nonzero entries, the proposed estimator performs more efficiently than a traditional approach. The method consists of three steps: (1) a classical Least Squares Estimate (LSE); (2) the support is recovered through a Linear Programming (LP) optimization problem which can be computed using a soft-thresholding step; (3) a de-biasing step using a LSE on the estimated support set. The main contribution of this note is a formal derivation of an associated ORACLE property of the final estimate. That is, with probability 1, the estimate equals the LSE based on the support of the true parameters when the number of observations goes to infinity.     

低信噪比下的直扩信号检测和多参数估计方法

直扩信号具有较低的功率谱密度,淹没在背景噪声中,信号侦察难度大.根据四阶矩一维切片可以有效地抑制高斯白噪声和充分包含直扩信号的载波、伪码信息这一特性,提出了一种基于四阶矩一维切片的直扩信号检测和多参数估计方法.该方法不需要任何先验信息,不仅能够检测直扩信号,还能同时估计载波频率、伪码周期和伪码速率等参数.仿真结果表明,这种方法可以在低信噪比下工作,具有一定的实用价值.     

On the frequency domain accuracy of closed-loop estimates

It has been argued that the frequency domain accuracy of high model-order estimates obtained on the basis of closed-loop data is largely invariant to whether direct or indirect approaches are used. The analysis underlying this conclusion has employed variance expressions that are asymptotic both in the data length and the model order, and hence are approximations when either of these are finite. However, recent work has provided variance expressions that are exact for finite (possibly low) model order, and hence can potentially deliver more accurate quantification of estimation accuracy. This paper, and a companion one, revisits the study of identification from closed-loop data in light of these new quantifications and establishes that, under certain assumptions, there can be significant differences in the accuracy of frequency response estimates. These discrepencies are established here and in the companion paper to be dependent on what type of direct, indirect or joint input-output identification strategy is pursued.     

Robust maximum-likelihood estimation of multivariable dynamic systems

This paper examines the problem of estimating linear time-invariant state-space system models. In particular, it addresses the parametrization and numerical robustness concerns that arise in the multivariable case. These difficulties are well recognised in the literature, resulting (for example) in extensive study of subspace-based techniques, as well as recent interest in ‘data driven’ local co-ordinate approaches to gradient search solutions. The paper here proposes a different strategy that employs the expectation-maximisation (EM) technique. The consequence is an algorithm that is iterative, with associated likelihood values that are locally convergent to stationary points of the (Gaussian) likelihood function. Furthermore, theoretical and empirical evidence presented here establishes additional attractive properties such as numerical robustness, avoidance of difficult parametrization choices, the ability to naturally and easily estimate non-zero initial conditions, and moderate computational cost. Moreover, since the methods here are maximum-likelihood based, they have associated known and asymptotically optimal statistical properties.