基于观测器的多智能体系统一致性控制与故障检测

针对一类受到未知干扰的非线性多智能体系统,提出了一种鲁棒一致性控制与故障检测算法.首先,针对每个智能体系统设计了一个未知输入非线性观测器.然后,基于观测器的状态估计信息,设计了鲁棒一致性控制协议.控制协议保证了给定的干扰抑制性能指标.接着,考虑智能体出现故障的情形,采用自适应阈值法,提出了一种分布式故障检测算法.最后,以多个直流电机驱动的单摆系统为例进行了仿真实验,仿真结果表明了一致性控制与故障检测算法的有效性.     

基于CPCRLB和一致性算法的分散式传感器管理

针对无线传感器网络中的目标跟踪问题,基于条件后验克拉美—罗下界（CPCRLB）提出一种分散式传感器节点管理方法．基于一致性策略给出一种CPCRLB的分布式迭代算法,并且基于分布式粒子滤波器给出该算法的数值逼近实现．对层次结构的无线传感器网络,将CPCRLB作为传感器管理的准则,基于平均一致性给出一种迭代的局部搜索算法,实现了无线传感器网络下观测节点的分散式在线选择．仿真结果表明了基于CPCRLB的分散式传感器管理方法在目标跟踪精度方面的有效性．     

The merits of exergy-based fault detection in petrochemical processes

The complexity and multi-domain nature of petrochemical (PC) plants make the application of conventional model-based fault detection and isolation (FDI) techniques a challenging endeavour. Although hybrid FDI schemes aim to address this shortfall, many are simply a combination of data-driven techniques that exclude physical system information. In this work, a hybrid approach to FDI of a PC process is proposed that is based on an exergy-data abstraction. Data from an actual system is abstracted to system exergy, based on physical knowledge of the system and then used as a diagnostic metric for the FDI scheme. In this paper, it is shown why energy-based approaches are lacking when considering petrochemical processes. After presenting a novel method for the real-time, automatic calculation of chemical exergy in Aspen HySys^® the applicability of exergy-based fault detection is investigated. Application of the exergy-based fault detection scheme shows a marked improvement over the energy-based approach with perfect detectability and isolability of the considered process faults. The exergy-based fault detection technique shows merit in comparison to the energy-based detection scheme. Additionally, and more importantly, exergy-based characterisation allows the use of more sophisticated model-based fault detection schemes to petrochemical processes.     

微电网系统的分布式优化下垂控制

针对微电网系统运行成本最优化问题,提出一种分布式优化下垂控制策略.首先,基于一致性理论,给出了一种分布式经济调度算法.采用矩阵摄动理论,分析了经济调度算法的收敛特性.其次,基于分布式优化调度解,设计一种新的分布式优化下垂控制器.在满足供需平衡以及各个发电单元运行约束的条件下,控制策略使得微电网系统运行成本最低.同时,提出的控制策略能够保证孤岛微电网的频率稳定在额定值.最后,通过仿真实例,验证了分布式优化下垂控制策略的有效性.     

Data-driven optimal switching and control of switched systems

In this paper, optimal switching and control approaches are investigated for switched systems with infinite-horizon cost functions and unknown continuous-time subsystems. At first, for switched systems with autonomous subsystems, the optimal solution based on the finite-horizon HJB equation is proposed and a data-driven optimal switching algorithm is designed. Then, for the switched systems with subsystem inputs, a data-driven optimal control approach based on the finite-horizon HJB equation is proposed. The data-driven approaches approximate the optimal solutions online by means of the system state data instead of the subsystem models. Moreover, the convergence of the two approaches is analyzed. Finally, the validity of the two approaches is demonstrated by simulation examples.     

A distribution independent data-driven design scheme of optimal dynamic fault detection systems

In this paper, design issues of data-driven optimal dynamic fault detection systems for stochastic linear discrete-time processes are addressed without precise distribution knowledge of unknown inputs and faults. Concerning a family of faults with different distribution profiles in mean and covariance matrix, we introduce a bank of parameter vectors of parity space and construct the parity relation based residual generators using process input and output data. In the context of minimizing the missed detection rate for a prescribed false alarm rate, the design of fault detection system is formulated as a bank of distribution independent optimization problems without posing specific distribution assumption on unknown inputs and faults. It is proven that the optimal selection of individual parameter vector can be formulated as a generalized eigenvalue–eigenvector problem in terms of the means and covariance matrices of residuals in fault-free and each faulty cases, and is thus solved via singular value decomposition. The tight upper bounds of false alarm rate and missed detection rate are simultaneously achieved quantitatively. Besides, the existence condition of the optimal solutions is investigated analytically. Experimental study on a three-tank system illustrates the application of the proposed scheme.     

基于自适应迭代学习算法的一类非线性系统故障检测与估计

针对迭代学习算法在非线性系统故障检测与估计过程中存在估计误差较大和收敛速度较慢等不足的问题,提出了一种基于龙格–库塔故障估计观测器模型的自适应迭代学习算法,有效降低了故障估计误差;并引入H∞性能指标,提高了故障估计观测器的收敛速度.该算法首先设计故障检测观测器对故障进行检测,然后设计故障估计观测器,并将自适应算法与迭代学习策略相结合,使得估计故障逐渐逼近真实故障,从而实现对非线性系统中多种常见故障的精确检测与估计.最后,通过机械臂旋转关节驱动电机的执行器故障仿真验证了所提算法的有效性.     

USSCD：一个基于均匀空间分割的快速碰撞检测算法

对于存在大量运动物体的虚拟环境，碰撞检测往往成为影响系统计算效率的瓶颈，为提高多体碰撞检测的效率，提出了一个基于均匀空间分割的快速多体碰撞检测算法——USSCD，该算法首先将物体空间均匀分割成一系列单元格，然后在每个单元格，通过基于AVL排序的扫描排除法进行碰撞检测，同时依据物体的分布密度，提出了一个计算单元格尺寸的优化方法，通过一系列实验，测试了USSCD算法的性能，并与I-COLLIDE算法进行比较，实验结果表明，在均匀分布条件下，当物体数量较大时，USSCD的效率高于I-COLLIDE算法，而且，USSCD算法的效率基本不受物体运动相关性的影响。     

Fault detection of sludge bulking using a self-organizing type-2 fuzzy-neural-network

Fault detection is important in the operation of wastewater treatment process (WWTP). In this paper, to ensure the process safety and effluent qualities, an intelligent fault detection (IFD) method, based on self-organizing type-2 fuzzy-neural-network (SOT2FNN) and intelligent identification method, was developed to detect and identify different types of sludge bulking. The main advantages of IFD are as follows. First, a data-driven framework, based on a data-driven model and an intelligent identification algorithm, was developed to facilitate the fault diagnosis. Second, a SOT2FNN, based on the intensity of information transmission algorithm and adaptive second-order algorithm, was designed to predict the sludge volume index (SVI) with high accuracy to provide necessary information for process monitoring. Third, an intelligent identification method, using the target-related identification algorithm (TRIA), was proposed to extract the correlation information to identify the types of sludge bulking. Finally, simulations and experimental examples were provided to confirm the effectiveness of the proposed IFD method.     

基于节点相似性的WSNs故障检测方法研究

针对目前多数无线传感器网络分布式故障检测的算法都以假设故障节点数据为离群值为基础,存在局限性的问题。提出一种基于节点相似度比较的无线传感器网络故障检测方法,簇头节点根据簇内节点数据的时空相关性,进行节点相似性度量,实时调整节点可信水平,并采用最优函数计算出当前实验的最优阈值(0.8)进行故障节点的判断。通过仿真实验证明:针对不同的故障模型,算法保持了良好的故障检测能力,一定程度上解决通用性问题。     

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     

基于贡献率法的非线性工业过程在线故障诊断（英文）

Over past decades, kernel principal component analysis(KPCA) appeared quite popularly in data-driven process monitoring area. Enormous work has been done to show its simplicity, feasibility, and effectiveness. However, the introduction of kernel trick makes it impossible to directly employ traditional contribution plots for fault diagnosis. In this paper, on the basis of revisiting and analyzing the existing KPCA-relevant diagnosis approaches, a new contribution rate based method is proposed which can explain the faulty variables clearly. Furthermore, a scheme for online nonlinear diagnosis is established. In the end, a case study on continuous stirred tank reactor(CSTR) benchmark is applied to access the effectiveness of the new methodology, where the comparisons with the traditional linear method are involved as well.     

基于贡献率法的非线性工业过程在线故障诊断

在过去几十年,核主成分分析（KPCA）已经广泛应用在数据驱动的过程监测领域. 大量的应用案例显示该算法简单、易用且有效. 然而,核函数的引入使得KPCA不能直接利用传统的贡献图方法进行故障诊断. 本文在重新审视和分析现有KPCA相关诊断方法的基础上,提出了一类新的贡献率方法,该方法能较清晰地解释故障变量. 在此基础上,建立了一套面向非线性在线故障诊断的框架. 最后,将该诊断框架应用到CSTR过程,结果显示该方法较传统的线性方法更有效.     

雾计算中基于DQL算法的伪装攻击检测方案

雾计算是一种在云数据中心和物联网（Internet of Things,IoT）设备之间提供分布式计算、存储等服务的技术,它能利用网络边缘进行认证并提供与云交互的方法。雾计算中以传统的安全技术实现用户与雾节点间安全性的方法不够完善,它仍然面对着窃听攻击、伪装攻击等安全威胁,这对检测技术提出了新的挑战。针对这一问题,提出了一种基于DQL（Double Q-learning）算法的雾计算伪装攻击检测方案。该方案借助物理层安全技术中的信道参数,首先在Q-learning算法的基础上处理Q值过度估计问题,获取最佳的伪装攻击测试阈值,然后通过阈值实现了用户与雾节点间的伪装攻击检测。实验结果表明,该算法检测伪装攻击的性能优于传统的Q-learning算法,具有在雾计算安全防护方面的优越性。     

Distributed estimation based on LQG control over homogeneous sensor networks

Motivated by navigation and tracking applications within sensor networks, we consider the distributed estimation problem over wireless sensor network. We propose a consensus based Kalman filtering algorithm based on optimal Linear Quadratic Gaussian control, in which each sensor can observe the dynamical system state, process the information data individually and communicate with each other within a sensing range. We provide a sufficient condition for the convergence of the proposed algorithm, and also give an upper bound for the estimation error covariance. Further, we find an optimal consensus gain for minimizing the network estimation error. Considering the occasional sensor fault and limited sensor energy, we investigate the proposed algorithm using only a subset of sensors to observe the dynamical system. With the assistance of the simulations, we verify the effectiveness of the proposed algorithms and present some interesting examples.     

动态内全潜结构投影的空间扩展故障检测方法

动态内偏最小二乘(DiPLS)方法是基于数据驱动的潜结构投影的动态扩展算法, 用于动态特征提取和关键 性能指标预测. 在大型装备系统中, 传感器采集的当前时刻样本受历史样本的影响且可能包含较大噪声. 在动态特 征提取中, 因DiPLS算法未按降序提取主成分, 导致残差空间仍存在较大变异, 动态和静态信息难以有效分离, 影响 故障检测性能. 为此, 本文提出了一种基于动态内全潜结构投影的故障检测方法(DiTPLS). 首先, 使用动态内偏最小 二乘方法和向量自回归模型建立动态模型并检测故障, 用于捕捉质量相关动态信息; 基于结构化动态主成分分析 算法建立一种改进的动态潜在变量模型, 用于残差分解, 提取质量无关的动态信息和静态信息, 并构造合适的统计 量进行故障检测. 数值仿真和田纳西–伊斯曼过程实验验证了DiTPLS算法的有效性.     

多因素自适应心跳检测算法研究

分布式系统中心跳检测是节点故障检测机制的关键技术之一,心跳频率设定的合理性将影响到故障检测的准确性和完整性。针对大数据环境下,分布式系统产生故障受到网络、节点、作业多方面影响,为了提高心跳频率在多方面因素影响下的合理性设定,提出一种多因素心跳检测综合指标评价模型。在该模型下同时考虑网络负载情况和节点CPU工作状态及节点作业的大小对心跳检测过程的影响。在此基础上,提出了基于多因素评价模型的自适应心跳检测算法。该算法可以随网络环境、节点CPU占用率、作业任务大小自适应地改变心跳频率,综合各因素给出心跳频率设定的最优方案。最后通过实验验证了多因素对心跳频率自适应调整的影响。     

Fault Detection Based On Online Probability Density Function Estimation

This study presents a new fault detection scheme based on the probability density function (PDF) of system output. Unlike the classical fault detection and diagnosis methods, in the proposed method, distribution of the system output is estimated online. To achieve this goal, an algorithm is introduced to estimate PDF online using fuzzy logic. Furthermore, convergence of this algorithm is investigated. Then, a residual is constructed that can show the existence of a fault in the system. The main advantages of the proposed method are robustness against measurement noise, even though it does not need the exact model and measured data of inputs and states. Simulation results show that this scheme can detect abrupt faults very well.     

基于自联想核回归的带离群值化工过程故障检测EI北大核心CSCD

基于数据驱动的故障检测模型通常要求训练数据必须是正常操作条件下的测量值.然而在实际工业生产过程中,即使在正常工况下,数据集中也难以避免存在离群值.此时若仍采用传统的基于多元统计分析的方法,其监测模型的控制限会受到严重影响,造成故障漏报.因此,为了确保当训练数据包含离群值时,监测模型仍然呈现较好的故障检测效果,本文提出了一种基于自联想核回归的故障检测方法.首先基于最小化β散度的鲁棒预白化算法对训练集进行白化计算,消除变量之间相关性对样本相似度度量的影响.然后通过自联想核回归算法重构正常工况下的验证数据,根据重构误差建立模型监测指标.为了消除离群值对故障样本重构的影响,构造截断函数来避免离群样本参与相似故障数据的重构,并对所有参与构建Q统计量的残差变量基于指数加权滑动平均方法自适应加权,得到新的监测统计量.将该方法运用于田纳西–伊斯曼过程并与其他方法进行比较,验证了本文所提故障检测算法的有效性.     

FCT网络的并发错误检测结构

文中提出了一种快速离散余弦变换电路的开发错误检测结构。为了达到１００％的故障覆盖率,ＦＣＴ采用基于第３类离散余弦变换的Ｂ．Ｇ．Ｌｃｅ算法蝶型结构实现。