基于迭代学习的离散线性时变系统故障诊断

针对一类离散线性时变系统的故障诊断问题,提出一种新的故障检测与估计算法.该算法通过引入虚拟故障构建离散故障跟踪估计器,在选取的优化时域内,利用估计器输出和系统实际输出产生的残差信号,采用迭代学习算法来调节虚拟故障,使虚拟故障逼近系统中实际发生的故障,从而达到对系统故障诊断的目的.该方法不仅能检测出系统不同类型的故障,还可以实现对故障信号的精确估计.仿真结果验证了所提出方法的有效性.     

离散时间线性时变系统的传感器故障估计滤波器设计

针对一类离散时间线性时变系统提出了一种传感器故障诊断方法.本文首先通过状态增广的方式将被研究的系统转化为描述系统的形式,并且基于该描述系统模型,采用方差最小化原则设计了一种能够同时估计系统状态和传感器故障的故障估计滤波器,然后利用一组故障估计滤波器提出了一种故障诊断方法.本文的主要贡献在于针对离散线性时变系统提出了一种不需要对故障动态进行假设的传感器故障诊断方法.所提出方法的另一个优点是该方法能够在存在过程和测量噪声的情况下实现故障检测、分离与估计.仿真结果说明了所提出方法的有效性.     

一种非线性非高斯随机系统的故障诊断方法

针对非线性非高斯随机系统在线故障诊断的问题,运用粒子滤波器提出了一种基于方差自适应粒子滤波器的非线性非高斯随机系统的故障诊断方法,可以用来解决系统的参数偏差型故障诊断问题。通过对连续搅拌釜式反应器(CSTR)的仿真研究,可以看出,该算法实现简单,易于对系统进行在线估计,对于发生缓变和突变的参数偏差型故障的检测与估计均较为有效。     

基于动态观测的随机离散事件系统故障诊断

近年来,离散事件系统故障诊断研究引起国内外学者广泛关注.鉴于此,研究动态观测下随机离散事件系统的故障诊断.首先引入一种动态观测,使事件的可观测性随着系统的运行而动态变化;然后分别对基于动态观测的随机离散事件系统的单故障可诊断性和模式故障可诊断性进行形式化;最后通过构造相应的诊断器,分别得到关于单故障可诊断性和模式故障可...     

离散事件系统间歇性故障的安全诊断

离散事件系统的间歇性故障诊断能够将系统中发生的间歇性故障及时诊断出来,但在诊断期间的系统可能会执行不安全操作.针对间歇性故障在诊断期间的安全性问题,提出一种基于事件的安全诊断方法.首先对发生间歇性故障的离散事件系统进行建模,并给出系统间歇性故障的安全可诊断性的形式化定义.然后通过构造非法语言识别器对系统的非法操作进行识别,并在此基础上构建一个安全验证器,由此得到一个关于系统间歇性故障安全可诊断性的充分必要条件,实现离散事件系统对间歇性故障的安全诊断.这种安全诊断既保证了间歇性故障一旦发生即能被及时诊断出来,又确保了在故障诊断期间系统不会执行任何不安全操作.     

不完备多模型混合系统故障诊断的粒子滤波算法

针对模型不完备的混合动态系统故障诊断问题提出了一种粒子滤波算法. 系统未建模动态利用未知故障模式描述, 当存在未知模式时, 常规的粒子滤波器算法存在发散现象. 本文分析了常规粒子滤波器发散的原因, 提取了两个基于粒子集合的统计量: 粒子集的规格化因子 W 以及最大后验概率估计状态的信度 B. 在此基础上设计了检测未知故障模式的阈值逻辑, 即当 W 几乎为0且 B 较小时离散状态为未知故障模式. 在一定假设下从理论上证明了算法的正确性. 通过不完备的非线性混合系统诊断问题验证了算法的有效性.     

随机多源干扰系统的复合DOBC和容错控制

针对一类带有多源干扰和常值故障的随机系统,研究其抗干扰和故障诊断问题.多源干扰包括由外源系统生成的部分信息已知的干扰和白噪声干扰两类.第1类干扰包含状态和干扰耦合,不仅可以代表一类部分信息已知的干扰,还可以代表一类随机干扰,且耦合增加了系统的复杂性,导致必然等价原则的无效.为了解决这个问题,提出复合极点配置和线性矩阵不等式(LMI)方法相结合的策略.首先,设计随机干扰观测器来估计第1类干扰;其次,设计随机故障诊断观测器来估计系统故障.基于此,结合容错控制和随机控制,提出基于观测器的复合容错控制策略,在满足一定条件下,该策略可以保证复合系统满足依均方渐近有界.最后,通过仿真验证所提出策略的正确性与有效性.     

飞机燃油系统传感器故障诊断方法研究

为了解决某飞机燃油系统传感器的故障检测与诊断问题,提出将小波分析法应用于飞机燃油系统传感器的故障诊断.通过传感器输出信号的一维离散小波变换,得到重构后的第一层细节系数.对该细节系数进行分析,可以精确定位故障点;进而分析传感器输出信号,可以判断出故障类型及故障程度.最后以该系统中油位传感器的故障诊断为例,利用地面半物理仿真试验中采集的现场数据,在Matlab环境下对该方法进行了有效性验证.     

分布式随机离散事件系统的模式故障预测EI北大核心CSCD

本文以随机自动机为模型,研究分布式随机离散事件系统的模式故障预测问题.首先根据分布式随机离散事件系统的分布特性对模式故障协同可预测性概念进行形式化,并通过构造模式故障识别器来识别系统中发生的模式故障.然后,构造一个模式故障协同预测器,提出一种基于协同预测器的具有多项式复杂度的算法,得到关于分布式随机离散事件系统模式故障协同可预测性的充分必要条件,实现对分布式随机离散事件系统的模式故障预测.     

一类基于状态估计的非线性系统的智能故障诊断

针对一类含有建模误差的非线性系统,研究了基于状态估计的智能故障诊断方法.首先提出一种状态估计器设计方法;然后在进行状态估计的同时用RBF神经网络来逼近系统所发生的故障.故障估计器的输入为系统的状态估计,所估计出的故障既可用作故障容错控制,也可用作报警.根据微分同胚,将含有建模误差的非线性系统变换为易于分析的规范形式,并在此基础上分析了故障诊断系统的稳定性和鲁棒性.仿真例子证明了该方法的有效性.     

混杂系统故障诊断的强跟踪滤波器方法

混杂系统是近年来研究的热点,针对混杂系统故障诊断难题,在对混杂系统描述的基础上,给出了混杂系统状态估计及离散模态识别的强跟踪滤波器算法,并将该算法扩展到状态与参数的联合估计,最后利用修正的Bayes算法作出故障判决,实现了混杂系统的故障诊断。通过对两容水箱典型混杂系统故障诊断的仿真实验,验证了该方法的有效性。     

Markov切换系统的随机稳定性分析

根据混合系统离散状态的动态行为和Markov链的状态也是离散的特点,提出了一类离散状态的动态行为是Markov链的混合系统。与传统的混合系统相比,这类系统能够刻画出混合系统离散动态行为的随机性,可以用来描述系统受到外界环境因素制约和内部突发事件等随机因素影响而发生变化的动态行为。根据动态系统的稳定性定义以及随机过程理论,给出了Markov线性切换系统的随机稳定性定义,并且分析了Markov线性切换系统的随机稳定性问题,给出了判定随机稳定性的充分必要条件。     

Fault tolerant control design via hybrid petri nets

This paper proposes a novel fault tolerant control (FTC) scheme for hybrid systems modeled by hybrid Petri nets (HPNs). The HPNs model consists of discrete and continuous PNs. The faults are represented by unobservable discrete transitions or the normal observable discrete transitions with abnormal firing time in discrete PNs. First, an observer‐based fault diagnosis method is proposed to estimate the marking in discrete places with unknown initial marking and diagnose the faulty behavior simultaneously. Then, an adaptive fault tolerant controller is designed to maintain the general mutual exclusion constraints (GMEC) of discrete PNs, and a scheme that adjusts firing speeds of continuous transitions is provided to maintain the optimality of continuous PNs. Finally, an example of an intelligent transportation system consisting of automated vehicles on a bridge is included to demonstrate the effectiveness of our developed techniques. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

模式转换未知的一类混杂系统的状态估计

Since the state of hybrid systems is determined by interacting continuous and discrete dynamics, the state estimation of hybrid systems becomes a challenging problem. It is more complicated when the discrete mode transition information is not available, and the modes of hybrid systems are nonlinear stochastic dynamic systems. To address this problem, this paper proposes a novel hybrid strong tracking filter (HSTF) for state estimation of a class of hybrid nonlinear stochastic systems with unknown mode transition, the method for designing HSTF is presented. The HSTF can estimate the continuous state and discrete mode accurately with unknown mode transition information, and the estimation of hybrid states is robust against the initial state. Simulation results illustrate the effectiveness of the proposed approach.     

Fault detection model‐based controller for process systems

This paper develops a model‐based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system can determine online the optimal control actions, detect faults quickly, and reconfigure the controller accordingly. Such a system can perform its function correctly in the presence of internal faults. A fault detection model based (FDMB) controller consists of two main parts: the first is fault detection and diagnosis (FDD) and the second is controller reconfiguration (CR). Systems subject to such abrupt failures are modeled as stochastic hybrid systems with variable‐structure. This paper deals with three challenging issues: design of the fault‐model set; estimation of hybrid multiple models; and stochastic MPC of hybrid multiple models. For the first issue, we propose a simple scheme for designing a fault model set based on random variables. For the second issue, we consider and select a fast and reliable FDD system applied to the above model set. Finally, we develop a stochastic MPC scheme for multiple model CR with soft switching signals based on the weighted probabilities of the outputs of different models. Simulations for the proposed FDMB controller are illustrated and analyzed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Comparison of Hybrid Systems and Fluid Stochastic Petri Nets

Hybrid Systems are modelsof interacting digital and continuous devices with applicationsin the control of aircraft, computers, or modern cars for instance.Concurrently, Fluid Stochastic Petri Nets (FSPNs) have been introducedas an extension of stochastic Petri nets to model physical systemsthat contain continuous fluid quantities controlled by discretelogic, as well as a means to deal with the possible state explosionof discrete models. Of course, an FSPN is by definition a hybridsystem but in this paper, we compare a general hybrid systemsformalism with FSPN formalism. The conclusion of this paper,suplemented by some examples, is that fluid stochastic Petrinets and hybrid system formalism can be converted from one tothe other. Although there is a price to pay for such a conversion,the conversion can transfer the solution methods from one tothe other and thus help the development of both formalisms.     

Integrated biological systems modeling: challenges and opportunities

Most biological systems are by nature hybrids consist of interacting discrete and continuous components, which may even operate on different time scales. Therefore, it is desirable to establish modeling frameworks that are capable of combining deterministic and stochastic, discrete and continuous, as well as multi-timescale features. In the context of molecular systems biology, an example for the need of such a combination is the investigation of integrated biological pathways that contain gene regulatory, metabolic and signaling components, which may operate on different time scales and involve on-off switches as well as stochastic effects. The implementation of integrated hybrid systems is not trivial because most software is limited to one or the other of the dichotomies above. In this study, we first review the motivation for hybrid modeling. Secondly, by using the example of a toggle switch model, we illustrate a recently developed modeling framework that is based on the combination of biochemical systems theory (BST) and hybrid functional Petri nets (HFPN). Finally, we discuss remaining challenges and future opportunities.     

混合动态系统及其应用综述

在概述混合动态系统产生背景与发展近况的基础上, 主要综述了混合动态系统研究中的若干重要问题, 包括描述混合动态系统的常见工具, 如自动机和Petri网, 以及混合动态系统的模型描述方法及用于描述混合动态系统的递阶结构模型和混合逻辑动态模型, 对混合动态系统控制器的设计和验证及其在故障诊断等方面的应用研究也作了介绍, 同时对混合动态系统的一些主要研究成果进行初步评价.     

基于系统参考模型的混合故障诊断技术研究

为满足复杂系统的故障诊断需求,提出了一种基于系统参考模型的混合故障诊断方法。针对不同系统中各节点的多种故障模式和交联关系,采用定性和定量的方法建立混合故障诊断参考模型。首先构建系统对象的故障元模型,然后建立对应的领域模型,最后基于故障诊断推理机实现对系统当前故障的诊断。通过构建某飞行数据设备的故障诊断模型,并进行仿真验证,结果表明该方法可以实时准确地判定系统故障,为提高复杂系统故障诊断的效率、提升系统运行安全提供了有效保障。     

基于混合随机Petri网的一类混合系统的模型及稳定性

混合动态系统是包含离散事件系统（ＤＥＳ）和连续变量系统（ＣＶＳ）的复杂系统。讨论了一类每个离散状态包含一个连续动态系统,且离散事件具有Ｍａｒｋｏｖ链性质的随机混合系统,在提出利用混合随机Ｐｅｔｒｉ网的建模方法的基础上,给出混合均方稳定的概念、稳定性和可镇定条件,最后以一个简单的例子做子说明和仿真。