红外热成像技术在电厂的应用

电力行业是目前我国民用红外热像仪应用最多的行业,红外热成像技术在电厂检测中应用广泛。依据红外热像测温和热像检测原理,讨论电力设备的发热原理、过热损耗的危害、故障的在线诊断方法以及影响热像测温的因素等。最后针对红外热像技术在电厂的应用进行讨论。     

红外热像检测技术的发展和研究现状

红外热像检测具有非接触性、实时性、测温范围广、无测温热惯性、测量结果直观形象等优势,得到各工程领域的青睐,广泛应用于设备故障、材料缺陷等的在线检测诊断。本文回顾和总结了红外热像检测技术国内外的发展和研究现状,展望了其未来的发展趋势。     

红外电力设备诊断技术对操作环境的要求

1.红外诊断技术在电力设备在线监测中的应用 由于电力设备长期运行在高电压、大电流及恶劣的环境中,其内部和外部的导电部位或绝缘介质容易发生劣化和各种缺陷,甚至形成事故隐患,因此,必须大力开展电力设备的在线式故障诊断.采用各种红外检测仪器,如红外测温仪、红外热像仪、红外热电视等,对电力设备的各种状态和指标进行在线检测,可以方便地得出正常或不正常的诊断结果.它可以在非接触的情况下,对正在运行中的电力设备进行检测,拍摄其温度场的分布,测量任何部位的温度值,然后诊断出各种外部故障及内部故障.红外诊断技术不仅能检测出各种接头发热,而且能检测出许多用常规方法无法检测出的内部故障,诸如高压断电器、避雷器、电力电容器、电力变压器、高压套管、互感器、电缆头、绝缘子、发电机、电动机等设备故障.     

红外热成像技术应用于高压输电设备缺陷诊断

红外热像仪能快速、实时地采用非接触手段在线监测和诊断出电力设备的大多数过热故障,防止电力设备损坏和由于这些设备损坏而导致的电网大面积停电事故发生。文章介绍了红外热成像技术的概念与发展、红外热像仪的测温原理、红外热成像技术在高压输电线路的诊断方式以及案例分析。全面推广红外热成像技术,对保障电力设备乃至电网的安全运行起到积极作用。     

红外热像技术及其应用的研究进展

论述了红外热像技术及其在温度(场) 测试方面的特点,红外热像设备的研究发展历程 及国内外的最新进展。对红外热像技术在材料或构件的无损检测与评价,电力和石化设备的状态热诊断,构(建) 物的状态检测与节能评测,电子元器件、PCB 板及整机的自动测试,地震、岩爆等自然灾害防治等方面的国内外应用研究进展的情况进行了简要评述,并展望了红外热像技术的应用前景。     

红外热像技术及其应用的研究进展

论述了红外热像技术及其在温度(场)测试方面的特点,红外热像设备的研究发展历程及国内外的最新进展。对红外热像技术在材料或构件的无损检测与评价,电力和石化设备的状态热诊断,构(建)物的状态检测与节能评测,电子元器件、PCB板及整机的自动测试,地震、岩爆等自然灾害防治等方面的国内外应用研究进展的情况进行了简要评述,并展望了红外热像技术的应用前景。     

基于多模态特征图融合的红外热图像目标区域提取算法

红外热图像目标区域(Region of Interest,ROI)提取对故障检测、目标跟踪等有着重要意义.为解决红外热图像干扰多、需人工标记及准确率低等问题,提出一种基于多模态特征图融合的红外热图像ROI提取算法.通过对比度、熵及梯度特征构建多模态特征图并进行区域填充,实现ROI提取.将新算法应用于实际采集的光伏太阳能板图像中.结果表明,新算法具有平均查准率高(93. 0553%)、平均查全率高(90. 2841%)、F1指数和J指数均优于图割法,人工标记少等优点,可有效用于红外热图像ROI提取.     

基于红外技术的一起110kV CVT内部发热故障分析

简述了基于红外检测及诊断技术的红外热像仪的检测原理,应用其发现了一起110kV电容式电压互感器(CVT)的内部发热缺陷.结合红外技术以及高压试验测试法,对该110kV CVT内部发热故障进行了分析,确定了故障原因.提出了改进及防范措施,避免此类设备缺陷引发事故.     

红外技术在电气设备检测中的应用

在传统条件下,我们无法对电气设备进行直接测温,不能及时发现设备过热现象。红外热像仪可以通过测温方法解决设备故障。本文着重论述了红外在线检测的原理和特点,并介绍了检测的实际例子和结果分析以及常用的检测方法。     

热壁加氢反应器在线红外热像检测与安全评定

利用红外热成像技术,在高温高压氢腐蚀理论研究和工程实践经验的基础上,利用红外热像在线检测的结果,实现了热壁加氢反应器方便而快捷的在线检测与安全评定。利用以上研究结果,对某厂的热壁加氢反应器,在运行状况下进行了表面温度场的红外热像测试和安全评定分析,工程应用效果良好。     

浅析国内基于红外技术的电路板故障检测方法

基于红外技术的电路板故障检测方法是一种全新的非接触式故障检测方法。结合电路板故障红外检测的基本组成要素,介绍了各种相关技术的基本知识和国内的研究情况。着重分析了红外信息提取过程中存在的一些不确定因素,提出了在状态识别中用支持向量机完成电路板故障红外智能诊断的方法。通过分析已有的研制成果,阐述了当前基于红外技术的电路板故障检测仪在实际应用过程中存在的不足,为电路板故障红外诊断技术的深入研究指出了关键点。     

基于改进Faster R CNN的光伏组件红外热斑检测算法

光伏故障检测对光伏电站智能运维具有重要意义。针对光伏组件红外图像中热斑目标小、难检测的问题,研究了基于改进Faster R CNN的光伏组件红外热斑故障检测模型。将Swin Transformer作为Faster R CNN模型中的特征提取模块,捕获图像的全局信息,建立特征之间的依赖关系,提高模型的建模能力;进一步利用BiFPN进行特征融合,改善了热斑故障由于目标小和特征不明显容易被模型忽略掉的问题;同时为了抑制光伏红外图像中背景和噪声的干扰,加入轻量级注意力模块CBAM,使模型更加关注重要通道和关键区域,提高对热斑故障检测精度。在自建光伏组件图像数据集上进行实验,热斑故障检测精度高达915,验证了本文模型对光伏组件热斑故障检测的有效性。     

基于红外图像的避雷器故障检测方法

在电力系统中,利用计算机视觉和图像处理技术对避雷器进行故障检测,在保障电力系统的安全运行方面具有非常重要的作用。提出了一种基于红外图像的避雷器故障检测方法。该方法首先对输入图像进行预处理,利用尺度不变特征变换（Scale-invariant feature transform,SIFT）描述子和K-means++算法训练视觉字典精确定位避雷器,然后利用线性谱聚类对选择出的区域进行分割,最后通过分析避雷器热像的特征,实现避雷器故障的检测。实验结果说明所提出的算法可以有效地检测避雷器故障。     

Test for detection and location of intermittent faults incombinational circuits

Faults in combinational circuits are either permanent or intermittent. Intermittent faults tend to be environment-dependent; hence altering the environment might rectify these faults. These faults can be detected by applying random input-vectors (IV). The existence of random intermittent faults might require applying more random IV before detection. The detection of permanent faults requires fewer random IV but correction demands location and replacement of the faulty device, if repair is not feasible. Thus correction of a permanent fault costs more than that of an intermittent fault. The correction cost can be reduced by detecting the type of fault. Since most operational failures in a circuit are due to intermittent faults, it is very important to detect the type of fault in order to find a cheaper solution. This paper discusses the behavior of permanent and intermittent faults in combinational circuits, and introduces a test-detection model (TDM) for these faults. The error latency for an intermittent fault is derived. Two test-strategies are intermixed in the model: random testing for fault-detection, and deterministic testing for deciding on the type of fault. The activity of intermittent faults that requires the minimum number of IV for detection is emphasized. Simulation is used to demonstrate the validity of TDM. Although the variables required in TDM can be difficult to evaluate, estimation of their values is not impossible. A worst-case analysis can always be adopted, where variables are easily evaluated, to find an upper bound on the error latency; thus detection of an intermittent fault is assured with a very high probability. The cost-saving offered by intermittent-fault corrections shows the practical aspect of TDM     

Induction machine fault detection using SOM-based RBF neural networks

A radial-basis-function (RBF) neural-network-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an RBF-type neural network for fault identification and classification. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting grid algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults.     

Design of self-testing and on-line fault detection combinational circuits with weakly independent outputs

In this article we propose a structure dependent method for the systematic design of combinational selftesting fault detection circuits that is well adapted to the arbitrarily chosen technical fault model. According to the fault model considered the outputs of the circuit are partitioned into different generally nondisjoint groups of weakly independent outputs. The parities of these groups of weakly independent outputs are compared in test mode as well as in normal operation mode with the corresponding predicted parities by use of a self-checking checker. For on-line detection, the hardware is in normal operation mode, and for testing, it is in test mode. In the test mode, these fault detection circuits guarantee a 100% fault coverage for single stuck-at-0/1 faults and a high fault coverage for arbitrary faults. In normal operation mode all technical faults considered will be detected possibly, with some degree of latency.Partially presented at the VLSI Test Symposium, Atlantic City, 1992.     

基于冗余有限域算术的AES S盒高效故障检测方案

为使AES S盒的多奇偶校验故障检测方案具备预期故障检测能力,提出了由预期故障覆盖率确定预测奇偶总数的参数计算模型.根据模型确定的预测奇偶总数,为基于冗余有限域算术的S盒定制了两种多分块多奇偶校验的故障检测方案.推导优化了各分块预测奇偶计算公式,并通过穷举搜索找到了使整个电路结构最优的多项式系数与映射矩阵.仿真结果表明两种方案的随机多故障覆盖率均约为97%,验证了参数计算模型的有效性,突发故障覆盖率分别约为61.8%、76.3%,优于已有文献中大部分故障检测方案.综合结果表明,对比于已有文献中具有相似故障检测能力的故障检测S盒电路,所设计电路的面积-延时积最小.     

Choice of Detection Parameters on Fault Detection in Wireless Sensor Networks: A Multiobjective Optimization Approach

In this paper, the intermittent fault detection in wireless sensor networks is formulated as an optimization problem and a recently introduced multiobjective swarm optimization (2LB-MOPSO) algorithm is used to find an optimum trade-off between detection accuracy and detection latency. Faulty sensor nodes are identified based on comparisons of sensed data between one-hop neighboring nodes. Time redundancy is used to detect intermittent faults since an intermittent fault does not occur consistently. Simulation and analytical results show that sensor nodes with permanent faults are identified with high accuracy and by properly choosing the inter-test interval most of the intermittent faults are isolated with negligible performance degradation.     

电子电路故障检测技术应用分析

电子电路在电子产品中有着重要作用,但电子电路会受到诸多因素的影响,可能会出现一些故障,文中结合工作中的实际情况探讨了电子电路故障检测技术的应用,以期增强电子产品的稳定性和可靠性。探究结果表明,电子电路在运行过程中可能会出现设计故障、内部故障等,这些故障是由设计不合理、人为操作不当等造成的。因此,应做好故障检测工作,通过观察法、电压测量法、电流测量法等先进的检测技术明确电子电路的故障类型与位置,为故障检修提供支持。     

On the generation of test patterns for multiple faults

This article presents a new method to generate test patterns for multiple stuck-at faults in combinational circuits. We assume the presence of all multiple faults of all multiplicities and we do not resort to their explicit enumeration: the target fault is a single component of possibly several multiple faults. New line and gate models are introduced to handle multiple fault effect propagation through the circuits. The method tries to generate test conditions that propagate the effect of the target fault to primary outputs. When these conditions are fulfilled, the input vector is a test for the target fault and it is guaranteed that all multiple faults of all multiplicities containing the target fault as component are also detected. The method uses similar techniques to those in FAN and SOCRATES algorithms to guide the search part of the algorithm, and includes several new heuristics to enhance the performance and fault detection capability. Experiments performed on the ISCAS'85 benchmark circuits show that test sets for multiple faults can be generated with high fault coverage and a reasonable increase in cost over test generation for single stuck-at faults.