一种基于SDG模型的传感器分布优化算法

传感器如何优化分布是故障诊断的重要课题.本文基于SDG模型提出一种传感器分布的优化算法.该算法利用SDG模型描述工艺流程,根据SDG模型的特征将节点进行归类并化简,得到最小的节点集,即整个SDG模型的自由度.在最小节点集包含的节点上安装传感器,就可以直接监控或间接推导出整个工艺流程中各变量的状态,从而为故障诊断奠定基础.通过实例研究.该算法切实可行.     

基于Django框架的故障诊断和安全评估平台

符号有向图(SDG)作为一种定性研究方法,在系统推理和图形绘制上有待改进.详细描述了计算机辅助下的SDG故障诊断和安全评估平台的开发过程.平台的开发基于开源Django框架,具有搭建维护简单、操作方便、易于二次开发等特点;利用分层SDG模型和新的推理算法实现对系统的故障诊断和安全评估,支持故障传播路径查询和分层SDG的自动绘制.以田纳西-伊斯曼过程(TEP)为例,对平台的有效性进行验证,结果证明平台推理结果完备、有效,具有实际应用的前景.     

基于趋势分析和SDG模型的故障诊断

基于趋势分析的故障诊断受数据影响较大,基于定向符号图(signed d irected graph)模型的故障诊断难于建模和推理,而两者结合以提高故障诊断效果的方法还未被考虑.本文将趋势分析的趋势信息和SDG模型的节点信息进行了比较,阐明了两者信息互补的特点,然后利用趋势分析建立SDG模型,并利用SDG模型改进基于趋势分析的故障诊断,建立了数据驱动方法和基于模型方法相结合的故障诊断方法.CSTR(continuous stirred-tankreactor)实例分析表明,基于趋势分析和SDG模型的故障诊断方法提高了诊断的准确性和精确性.     

基于KPCA的半定量SDG过程故障诊断方法应用研究

近年来,过程工业安全事故频发,这使得加强生产过程安全保障变得迫在眉睫,而对于过程故障的监测、诊断是有效规避故障产生严重后果的一个有效方法。本文提出了一种基于核主成分分析(KPCA)的半定量符号有向图(SDG)故障诊断方法。此方法运用KPCA对过程进行异常检测,当找到异常过程变量后,通过引入相对偏移率和分类诊断对传统SDG进行改进,从而得到故障的完整传播路径,为故障诊断以及后续的故障处理提供了有效的指导。通过在TE过程中的仿真验证,结果表明,本方法诊断效率高,精确度高,为保证生产安全运行,提高产品质量提供了新途径。     

飞控系统传感器故障诊断的神经网络方法研究

BP神经网络对于飞行控制系统传感器故障诊断是一种有效的故障模式识别方法;在标准BP神经网络的基础上,提出了一种新的BP改进算法——自适应FMBP算法(SAFMBP),用以消除标准BP网络收敛速度慢及易陷入局部极小等缺点,并且建立了飞行控制系统仿真模型和传感器常见故障模型,采用基于神经网络模式分类的故障诊断方法,应用改进的BP神经网络(SAFMBP)进行飞控系统传感器的故障诊断,最后给出了仿真诊断实例。     

模糊SDG故障诊断方法及其应用

基于SDG的故障诊断方法在使用过程中,由于阈值设定不合理会导致故障的误报或漏报。针对该问题展开研究,提出模糊SDG模型,建立五级SDG模型并引入参数模糊隶属度,提出相应的模糊推理算法。通过将模糊SDG模型及其推理方法应用于某常减压蒸馏装置进行故障诊断实例分析,验证了方法的有效性和可行性。     

一种改进的DFD无线传感器网络节点故障诊断算法研究

无线传感器网络是远程分布式环境监测的重要手段。节点故障诊断作为无线传感器网络的关键技术之一,是无线传感器网络大多应用领域不可或缺的环节。DFD算法是一种可应用于无线传感器网络的节点故障诊断算法,通过网络内邻居节点间的数据交换和相互测试诊断出故障节点。对DFD算法的诊断判据进行了修改,提出了改进的DFD算法。仿真结果表明,改进的DFD算法能适用于待诊断节点的邻居节点数较少、节点故障率较高的传感器网络,且大大提高了故障诊断精度。     

基于改进算法的供热锅炉房动力装置故障诊断研究

传统的供热锅炉房动力装置故障诊断方法在诊断过程中容易受到外界温度的影响,使其附近的供热管网热损耗过高,造成资源浪费。为此,该文设计基于改进算法的供热锅炉房动力装置故障诊断方法。利用压力传感器和转速传感器监测供热锅炉房动力装置状态参数,通过数据采集卡将参数数据传送至上位动力装置中,修正改进的神经网络算法中的连续权值和节点阈值,将采集的参数数据作为输入,利用阈值判定函数判断输出结果,完成供热锅炉房动力装置故障诊断。实验结果表明,该文方法在诊断过程中,随着温度的变化,其附近的供热管网热损耗始终处于低水平状态,没有发生提高现象,避免了资源浪费。     

基于自适应神经模糊推理的传感器在线故障诊断

介绍了一种应用于故障诊断的基于T-S模糊模型的自适应模糊神经网络。给出了网络的连接结构和学习算法以及传感器故障在线诊断原理。仿真结果表明，该方法具有收敛速度快，诊断精度高，泛化能力强的特点，而且可以诊断系统多种工作过程传感器的在线故障。     

基于符号模型检测的SDG故障诊断解形式化验证

由于定量信息和非线性因果关系的丢失,SDG的故障诊断解需要进一步的进行校核与验证。创新地将SDG故障诊断解的验证置于符号模型检测框架中进行研究,提出了基于符号模型检测的SDG故障诊断形式化验证方法。首先扩展、转换了SDG模型的有限状态变迁系统形式化描述,建立了SMV模型;其次引入故障传播时间建立了模型观测变量的动态验证信息,并基于步进式监控分析了动态验证策略,将SDG正向推理扩展建模为动态推理验证;然后面向符号模型检测扩展了动态推理验证过程的SMV模型,提出了验证算法SSDGFD_ SMC;最后,通过一个实例验证了算法的有效性。     

大型复杂系统的动态SDG模型及传感器布置问题

符号有向图(SDG: signed directed graph)可以用于描述大型复杂系统及其变量之间的因果影响关系, 但是在描述故障传递关系时不能表示其动态传播规律. 本文在SDG支路上引入时间参数, 用于近似描述变量的变化在系统中的传递时间, 由此构建的SDG模型称为动态SDG模型. 另外, 要实现故障检测需要有传感器的信息, 传感器的布置直接影响着故障检测的性能. 本文在动态SDG框架下, 研究了故障的可检测性和可分辨性问题, 提出了一些一般性的结论, 并给出通过正向推理来求故障传播过程和传感器布置方案的方法. 最后, 通过实例验证该方法的有效性.     

Sensor deployment based on fuzzy graph considering heterogeneity and multiple-objectives to diagnose manufacturing system

In this paper, a methodology is presented to generate an optimized sensor deployment deciding sensor types, numbers, and locations to accurately monitor fault signatures in manufacturing systems. Sensor deployment to robustly monitor operation parameters is the corner stone for diagnosing manufacturing systems. However, current literature lacks investigation in methodologies that handle heterogeneity among sensor properties and consider multiple-objective optimization involved in the sensor deployment. We propose a quantitative fuzzy graph based approach to model the cause–effect relationship between system faults and sensor measurements; analytic hierarchy process (AHP) was used to aggregate the heterogeneous properties of the sensor–fault relationship into single edge values in fuzzy graph, thus quantitatively determining the sensor's detectability to fault. Finally sensor–fault matching algorithms were proposed to minimize fault unobservability and cost for the whole system, under the constraints of detectability and limited resources, thus achieving optimum sensor placement. The performance of the proposed strategy was tested and validated on different manufacturing systems (continuous or discrete); various issues discussed in the methodology were demonstrated in the case studies. In the continuous manufacturing case study, the results illustrated that compared with signed directed graph (SDG), the proposed fuzzy graph based methodology can greatly enhance the detectability to faults (from SDG's 0.699 to fuzzy graph's 0.772). In the discrete manufacturing case study, results from different optimization approaches were compared and discussed; the detectability of sensors to faults also increased from SDG's 0.61 to fuzzy graph's 0.65. The two case study results show that the proposed approach overcame the qualitative approach such as signed directed graph's deficiency on handling sensor heterogeneity and multiple objectives; the proposed approach is systematic and robust; it can be integrated into diagnosis architecture to detect faults in other complex systems.     

图实现算法综述与评测分析

图实现(Graph realization)问题研究基于节点间全部或部分距离关系测量, 在$d$维空间中计算图的顶点坐标, 使得在所实现图中各节点之间实现距离与测量距离尽可能一致.图实现问题是一个典型的优化问题, 在传感器网络定位、蛋白质结构重建、数据可视化、社交网络分析、机器人同步定位与构图等领域有着广泛应用.图实现的研究同图刚性理论有着紧密的联系, 图的刚性与全局刚性决定图的可实现性.在可实现图中, 现有工作提出几类典型的代表性图实现算法, 包括: 1)基于三边测距类方法; 2)求解距离方程类方法; 3)基于全局优化类方法; 4)基于模块拼合类方法.本文对图实现的刚性理论, 四类图实现算法的设计思想、适用条件、算法流程等进行综述分析, 通过实验对算法进行准确性、计算复杂度、可靠性等方面的比较和分析.     

基于多块KPCA和SDG 的故障诊断方法

针对大规模复杂工业过程,提出一种基于多块核主元分析(MBKPCA)和符号有向图(SDG)的故障诊断方法。首先,提出基于SDG和优先级的分块策略,以强连接元SCC为最高优先级、多入/出度节点群为次高优先级、节点链为最低优先级对过程进行分块;在此基础上,采用MBKPCA进行过程监控,对于检测到的故障,先确定故障发生在哪一个数据块,再触发SDG在故障块内完成故障定位。所提出方法克服了多块KPCA故障隔离不完全和SDG推理过程中组合爆炸的缺点,可以提高复杂工业过程故障诊断的准确度和速度。基于Tennessee Eastman过程的仿真研究表明了所提出故障诊断方法的有效性。     

基于维诺图和二分图的水面移动基站路径规划方法

水面传感器网络（Surface sensor networks,SSNs）具有节点稀疏布置的特点（节点间距离通常大于节点通信半径）,因此难以通过节点间的多跳路由汇聚数据,目前主要采用移动基站（Mobile sink,MS）收集网络中的数据,其中移动基站的路径规划是一个关键问题.该文提出一种基于维诺图和二分图的水面移动基站路径规划方法,首先利用维诺图理论生成数据收集“候选点”;然后以二分图描述候选点对网络中传感器节点的支配关系,并基于支配集理论求解出“最小有效支配集”,即可以收集网络中所有节点数据的最小的候选点集合;最后针对最小有效支配集形成最优路径.大量实验结果表明该方法可以有效地规划出水面传感器网络中移动基站的路径,不仅可以完成全网数据收集任务,而且具有路径长度短、能量效率高和节点能耗均衡的优点.     

On the Planarization of Wireless Sensor Networks

Network planarization has been an important technique in numerous sensornet protocols—such as Greedy Perimeter Stateless Routing (GPSR), topology discovery, data-centric storage, etc.—however the planarization process itself has been difficult. Known efficient planarization algorithms exist only for restrictive wireless network models: unit-disk graphs with accurately known location information. In this paper, we study efficient planarization of wireless sensor networks, and present a novel planarization method for a more general network model, where sensors can have non-uniform transmission ranges and no location information is needed. Our planarization algorithms also include a (2+ε)-approximation algorithm and an FPT algorithm for the bipartite planarization problem.     

基于符号定向图的计算机辅助自动建模研究

分析了符号定向图(Signed Directed Graph,SDG)技术在危险与可操作性分析(HAZard and OPembility,HAZOP)、过程危险性分析(Process Hazard Analysis,PHA)等领域中的应用现状和前景,提出了研究基于SDG的HAZOP计算机辅助自动建模方法,构建了基于SDG的HAZOP计算机辅助自动建模软件平台,阐述了研究路线,展示了应用策略。软件平台构建的研究填补了国内在SDG自动建模领域的研究空白,拓展了计算机辅助设计(CAD)技术和符号定向图(SDG)技术在复杂系统重大事故及智能诊断领域的应用。     

Delay characterization and performance evaluation of cluster-based WSN with different deployment distributions

Provisioning of quality of service (QoS) is the ultimate goal for any wireless sensor network (WSN). Several factors can influence this requirement such as the adopted cluster formation algorithm. Almost all WSNs are structured based on grouping the sensors nodes into clusters. Not all contemporary cluster formation and routing algorithms (e.g. LEACH) were designed to provide/sustain certain QoS requirement such as delay constraint. Another fundamental design issue is that, these algorithms were built and tested under the assumption of uniformly distributed sensor nodes. However, this assumption is not always true. In some industrial applications and due to the scope of the ongoing monitoring process, sensors are installed and condensed in certain areas, while they are widely separated in other areas. Also unlike the random deployment distributions, there are many applications that need deterministic deployment of sensors like grid distribution. In this work, we investigated and characterized the impact of sensor node deployment distributions on the performance of different flavors of LEACH routing algorithm. In particular, we studied via extensive simulation experiments how LEACH cluster formation approach affects the delay (inter and intra-cluster delay) and energy efficiency expressed in terms of packet/joule for different base station locations and data loads. In this study, we consider four deployment distributions: grid, normal, exponential and uniform. The results showed the significant impact of nodes distribution on the network energy efficiency, throughput and delay performance measures. These findings would help the architects of real time application wireless sensor networks such as secure border sensor networks to design such networks to meet its specifications effectively and fulfill their critical mission.