便携式雷达综合检测平台及智能故障诊断系统的设计

为满足某型雷达装备野战基层级维修保障需求,设计并研制了便携式雷达综合检测平台。该检测平台采用多微机共总线分布式控制、基于BP神经网络的智能故障检测系统、直接数字合成和模拟仿真等技术,集某型雷达故障检测、性能测试和模拟训练功能于一体。实现了检测平台的集成化、小型化和检测智能化,解决了某型雷达现有维修设备体积庞大、配套设备繁杂、机动性差、价格昂贵、操控复杂等问题,经实验验证,提高了雷达在部队实装配备的综合诊断检测效率。     

火电厂智慧水务关键技术及信息平台建设研究

针对火电厂耗水量大、能耗物耗高、水复用率低、智能化水平低等关键科学技术难题,基于智慧水务研究成果,提出火电厂智慧水务关键技术及信息平台建设的研究方法。根据火电厂水务信息化面临的现状及形势,确立研究的总体、具体目标。融合现场监测资料和智能分析理论和方法,提出水量平衡智能测算方法,建立火电厂污水处理主要设备的故障诊断及寿命预测智能方法,构建火电厂水处理系统历史和实时成本测算模型。提出了信息平台的网络架构、体系架构和逻辑架构设计方案,信息平台融合火电厂水务管理专家辅助决策系统、火电厂系统运行关键设备及构筑物可靠性分析与评价模型等。为实现集节水型、节能型、环保型、安全型、经济型为一体化的智能火电厂水务管理提供技术支撑。     

企业离散式智能设备预测性维护综述

针对智能设备的大量使用且缺乏根据监测大数据进行故障自动分析、判断与处理的问题,研究了基于物联网技术、大数据技术、边云协同技术的智能设备预测性维护框架和模式.提出针对非智能设备安装传感器实现设备智能化的方法.指出边缘计算负责设备工况数据的实时采集、分析,可快速甄别设备故障并实时报警;云计算聚焦同类设备运行海量历史数据的挖掘和分析,形成故障自动预测分析和诊断模式并下载至智能边缘设备.在研究了模型驱动、数据驱动、概率统计驱动、数字孪生和概率数字孪生驱动等故障预测模式后,提出了采用数据驱动的多层级数据融合模式,为制定企业性智能设备维保方案提供借鉴作用.     

An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds

Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM-Grid, whose construction is based on a support vector machine (SVM). SVM-Grid is used to optimize parameters in SVM. Proper attributes of a cloud system's running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.      

基于PSO-SVM的电机故障检测

传统智能故障检测模型中算法初始参数复杂,选取难度较大,缺乏自学习、自组织能力、泛化能力弱,极易陷入局部极小值、算法单一等缺点.组合应用智能检测算法可整合不同算法优势,避免单一算法缺点,为此,文中提出支持向量机算法与改进粒子群算法相结合的电机故障检测模型:以电机故障特征频率特征数据为基础,首先使用改进全局求解性能的粒子群算法求解影响支持向量机分类检测性能的最佳参数,然后把最佳参数应用于的擅长模式识别的支持向量机算法,进行样本数据的训练,构建故障检测模型;最后,使用故障检测模型对电机的状态进行预测.实验结果表明,采用该方法进行故障检测的准确率,比传统的神经网络方法提高17%,比纯支持向量机算法提高3.33%.     

面向电厂关键设备故障知识图谱构建的关系抽取方法研究

当今电厂面临着诸多挑战，包括电力设备种类繁多、设备数量庞大、故障类型众多、数据耦合关系复杂以及海量的故障信息数据等。知识图谱能够将各种信息整合、可视化呈现，并支持智能化应用，有助于人们更好地获取、管理和应用知识，从而提高效率、创造价值。运用知识图谱来分析电厂故障数据，有助于深入研究电厂设备故障情况。在构建知识图谱的过程中，关系抽取是关键步骤之一，其准确率直接影响最终知识图谱构建的质量。本文提出了一个面向电厂关键发电设备故障知识图谱构建的关系抽取工具，该工具能将故障信息中海量、异构的数据以及相关故障处理进行可视化表达，同时支持用户交互式地参与到关系抽取的过程中，通过迭代训练来优化关系抽取模型。在实验测试阶段，利用真实电厂设备故障数据进行验证，证明了该工具在显著提高关系抽取的准确率方面的有效性。因此，构建的知识图谱质量得以提升，为电厂管理人员更好地运维管理发电设备提供了重要支持，为管控电厂相关数据以及推动电厂完备建设提供有力支撑。     

刹车控制单元便携式检测设备的设计与实现

在飞机地面试验台进行刹车试验时,或者在外场飞机起飞前进行检查、大修厂的飞机维修时,防滑刹车控制单元的性能检测设备必不可少。针对飞机预测及健康管理(PHM)提出的维修保障困难问题,于开放的、面向对象的PHM体系结构,提出了一种便携式飞机刹车系统检测设备,结合其关键系统展开设计。此设计包含了飞机PHM体系结构设计及相关环节的技术实现,并将传统测试的相关测试设备小型化轻量化,集成在一拉杆箱内,加以一体化和良好的散热设计,使得设备方便运输携带,且具有一定抗强冲击力和震动的特性,有很高的安全性、可靠性,并具备故障定位等智能化检测能力,可满足外场检测的恶劣测试环境,提高飞机检修效率。     

基于互联网平台的配电网智能化运维管理模式

为了有效运维管理配电网, 获取配电网运行状态的全部数据以及配电网中电力设备可能出现异常及故障的情况, 提高配电网经济效益, 提出基于互联网平台的配电网智能化运维管理模式. 通过互联网平台融合其他专业系统数据, 构成配电网智能化运维管理平台, 采用归一化谱聚类算法, 分析多维状态量的历史正常数据和异常数据, 获取历史数据曲线的形状系数和轮廓系数, 提取多维状态量故障特征, 利用知识发现子模块与决策器设计子模块, 分析配电网中电力设备健康度等级, 根据关联规则挖掘, 获取不同电力设备的重要度指数, 评估运维决策风险, 实现配电网智能化运维决策管理. 实验结果表明: 所研究模式能够有效获取配电网运行的实时数据, 及时发现可能出现异常及故障的电力设备, 提高配电网经济效益.     

基于犹豫模糊矩阵与变异算子的变电设备故障检修仿真

为降低变电设备故障检修时的综合风险成本,提出基于犹豫模糊矩阵与变异算子的变电设备故障检修方法。设置犹豫模糊矩阵,提取变电设备振动信号特征,将特征值输入稳定的Hopfield神经网络,分类诊断变电设备的故障;通过基于变异算子的变电设备故障检修优化模型,构建目标为综合风险成本的函数,设置约束条件为电网停电次数为1次、传输功率不越限,获取符合检修目标和约束条件的检修最优方案。实验仿真结果显示：所提方法可优化变电设备故障检修方案,提升变电设备故障诊断效率,保证设备检修的停电次数为每月1次,降低电网综合风险成本。     

风洞群真空泵组集中监测与智能故障诊断系统的设计

真空是风洞群开展试验必备的动力资源;作为风洞群动力保障之一,真空泵组具有数量多、分布分散、结构复杂、集成度高、运行噪声大等特点;为满足真空泵组设备健康管理和维修决策支持的需要,设计并研制了风洞群真空泵组集中监测与智能故障诊断系统;该系统采用PLC隔离通信、数据可视化、设备状态监测及智能预警、基于故障机理和规则推理的专家系统等技术,实现了真空泵组的集中监测和故障智能诊断,解决了风洞动力设备故障预判和提前预警困难、故障定位不易准确、缺乏专家知识库等问题;经实用验证,该系统实现了风洞群真空泵组故障智能预警和故障诊断分析,提高了综合诊断监测效率,值得推广。     

Multiple partial discharge source discrimination with multiclass support vector machines

The costs of decommissioning high-voltage equipment due to insulation breakdown are associated to the substitution of the asset and to the interruption of service. They can reach millions of dollars in new equipment purchases, fines and civil lawsuits, aggravated by the negative perception of the grid utility. Thus, condition based maintenance techniques are widely applied to have information about the status of the machine or power cable readily available. Partial discharge (PD) measurements are an important tool in the diagnosis of power systems equipment. The presence of PD can accelerate the local degradation of insulation systems and generate premature failures. Conventionally, PD classification is carried out using the phase resolved partial discharge (PRPD) pattern of pulses. The PRPD is a two dimensional representation of pulses that enables visual inspection but lacks discriminative power in common scenarios found in industrial environments, such as many simultaneous PD sources and low magnitude events that can be hidden below noise. The literature shows several works that complement PRPD with machine learning detectors (neural networks and support vector machines) and with more sophisticated signal representations, like statistics captured in several modalities, wavelets and other transforms, etc. These methods improve the classification accuracy but obscure the interpretation of the results. In this paper, the use of a support vector machine (SVM) operating on the power spectrum density of signals is proposed to identify different pulses what could be used in an online tool in the maintenance decision-making of the utility. Particularly, the approach is based on an SVM endowed with a special kernel that operates in the frequency domain. The SVM is previously trained with pulses of different PD types (internal, surface and corona) and noise that are obtained with several test objects in the laboratory. The experimental results demonstrate that this technique is highly effective in identifying PD for cases where several sources are active or when the noise level is high. Thus, the early identification of critical events with this approach during normal operation of the equipment will help in the decision of decommissioning the asset with reduced costs and low impact to the grid reliability.     

基于大数据的电力企业资产组维修策略优化研究

汇总过去若干年的电力设备故障数据,运用大数据分析方法,把故障预测技术引入到预防性维修的实践中,提出一种基于大数据的预防性维修策略。首先,根据由状态检测信息得到剩余寿命的预测结果,以预防性维修时的剩余寿命为阀值制定预防性维修策略。然后,根据更新过程理论,建立以电力设备的预防性维修阀值和预测间隔期为优化变量,综合考虑电力设备维修成本、客户满意度、电量销售、停电损失、维修时机选择等约束条件呢,以电力设备平均维修费用最小和电力设备可用度最大为优化目标的预防性维修优化模型。采用人群搜索算法进行优化求解,得到系统最佳的预防性维修阀值和维修预测间隔期。最后,通过引入算例,对所建模型优化仿真求解,得到电力设备最佳的预测周期,在保证电力设备可用度的同时,使电力设备的平均维修费用最小,验证了所建模型的可行性和有效性,从而提高电力企业的整体效益。     

Intelligent state estimation for fault tolerant nonlinear predictive control

There is growing realization that on-line model maintenance is the key to realizing long term benefits of a predictive control scheme. In this work, a novel intelligent nonlinear state estimation strategy is proposed, which keeps diagnosing the root cause(s) of the plant model mismatch by isolating the subset of active faults (abrupt changes in parameters/disturbances, biases in sensors/actuators, actuator/sensor failures) and auto-corrects the model on-line so as to accommodate the isolated faults/failures. To carry out the task of fault diagnosis in multivariate nonlinear time varying systems, we propose a nonlinear version of the generalized likelihood ratio (GLR) based fault diagnosis and identification (FDI) scheme (NL-GLR). An active fault tolerant NMPC (FTNMPC) scheme is developed that makes use of the fault/failure location and magnitude estimates generated by NL-GLR to correct the state estimator and prediction model used in NMPC formulation. This facilitates application of the fault tolerant scheme to nonlinear and time varying processes including batch and semi-batch processes. The advantages of the proposed intelligent state estimation and FTNMPC schemes are demonstrated by conducting simulation studies on a benchmark CSTR system, which exhibits input multiplicity and change in the sign of steady state gain, and a fed batch bioreactor, which exhibits strongly nonlinear dynamics. By simulating a regulatory control problem associated with an unstable nonlinear system given by Chen and Allgower [H. Chen, F. Allgower, A quasi infinite horizon nonlinear model predictive control scheme with guaranteed stability, Automatica 34(10) (1998) 1205–1217], we also demonstrate that the proposed intelligent state estimation strategy can be used to maintain asymptotic closed loop stability in the face of abrupt changes in model parameters. Analysis of the simulation results reveals that the proposed approach provides a comprehensive method for treating both faults (biases/drifts in sensors/actuators/model parameters) and failures (sensor/ actuator failures) under the unified framework of fault tolerant nonlinear predictive control.     

SVM practical industrial application for mechanical faults diagnostic

A large percentage of the total induction motor failures are due to mechanical faults. It is well known that, machine’s vibration is the best indicator of its overall mechanical condition, and an earliest indicator of arising defects. Support vector machines (SVM) is also well known as intelligent classifier with strong generalization ability. In this paper, both, machine‘s vibrations and SVM are used together for a new intelligent mechanical fault diagnostic method. Using only one vibration sensor and only four SVM’s it was achieved improved results over the available approaches for this purpose in the literature. Therefore, this method becomes more attractive for on line monitoring without maintenance specialist intervention. Vibration signals turns out to occur in different directions (axial, horizontal or vertical) depending on the type of the fault. Thus, to diagnose mechanical faults it is necessary to read signals at various positions or use more them one accelerometer. From this work we also determined the best position for signals acquisition, which is very important information for the maintenance task.     

Genetic Algorithm Training of Elman Neural Network in Motor Fault Detection

Fault detection methods are crucial in acquiring safe and reliable operation in motor drive systems. Remarkable maintenance costs can also be saved by applying advanced detection techniques to find potential failures. However, conventional motor fault detection approaches often have to work with explicit mathematic models. In addition, most of them are deterministic or non-adaptive, and therefore cannot be used in time-varying cases. In this paper, we propose an Elman neural network-based motor fault detection scheme to address these difficulties. The Elman neural network has the advantageous time series prediction capability because of its memory nodes, as well as local recurrent connections. Motor faults are detected from the variants in the expectation of feature signal prediction error. A Genetic Algorithm (GA) aided training strategy for the Elman neural network is further introduced to improve the approximation accuracy, and achieve better detection performance. Experiments with a practical automobile transmission gearbox with an artificial fault are carried out to verify the effectiveness of our method. Encouraging fault detection results have been obtained without any prior information on the gearbox model.     

Leveraging on causal knowledge for enhancing the root cause analysis of equipment spot inspection failures

Causal correlation data over the equipment spot-inspection operation and maintenance (O&M) records and fault investigation sheets potentially reflect the state related to the causal effect of equipment failures. Various factors influence equipment failures, making it difficult to effectively analyze the main cause of the problems. Mining and leveraging these causal data from the equipment spot inspection records will undoubtedly significantly improve the root cause analysis of the fault in the O&M system. Hence, this paper introduces causal knowledge in equipment fault O&M for the first time and proposes to exploit causal knowledge for enhancing root cause analysis of equipment spot inspection failures. Specifically, an equipment fault O&M knowledge graph with causal knowledge called CausalKG is constructed to provide knowledge support for the causal analysis of faults. That is, CausalKG consists of spot-inspection knowledge graph (SIKG) and causal relationship knowledge (CRK) in equipment fault O&M. Further, a CausalKG-ALBERT knowledge reasoning model is designed. The model transforms CausalKG into network embeddings based on relational graph convolutional networks. In turn, it combines the Q&A mechanism of the language model ALBERT to mine the root cause knowledge of equipment failures. The case study confirms that incorporating the CRK is more effective than directly using the SIKG for causality reasoning; The model can fully use causal relationship knowledge to enhance the reliability of root cause analysis. This method is valuable to help engineers strengthen their causal analysis capabilities in preventive equipment maintenance.     

基于模糊控制和大数据算法模型的电力运维故障诊断设备方法

针对现有技术中对电力运维故障检测灵敏度低、诊断误差大等问题,设计了一种新型故障诊断方案。该方案将PID模糊控制计算器与大数据算法模型相结合,并采用实时布线的方法减少诊断面积,基于改进型大数据算法模型提取电力运维设备故障数据特征,对电力运维设备运行工况构建诊断网络,通过分析电力运维设备工况的功能系统完成数据诊断。为了减少诊断误差,该研究设计了一种故障诊断设备,采用集成芯片化设计和算法程序,减小体积的同时保证检测结果的准确性。实验结果表明,该研究方法故障诊断误差小,准确率最高达到98.6%。     

Conceptual design of remote monitoring and fault diagnosis systems

Condition monitoring and fault diagnosis are of fundamental importance for many industrial systems. In the last decade, substantial research efforts have been made on the surveillance and diagnosis systems for different types of equipment, with the approach of integrating information technologies and intelligent computing methods. This paper presents the conceptual design of a distributed information system of condition monitoring and fault diagnosis for a growing number of gas turbine-based power generation systems. Each individual information system that monitors a specific gas turbine system, locally deployed in a power plant, is linked to another information system, deployed at the manufacturer's site, which oversees all the gas turbine systems in parallel. The systems are constructed on the basis of COM components, which are conceptually separated into three tiers. Subsequently, this paper proceeds to present a generic business domain model with components encapsulating physical entities of interest. Finally, this paper addresses the interactions among components, which considerably affect the performance of the system including efficiency and effectiveness. It has been identified that both asynchronous and synchronous communication mechanisms are required for exchanging information for different scenarios. COM+ services are also required for supporting object pooling, transaction coordination, and security control.     

飞控蓄电池盒故障检测系统的研究与设计

飞控蓄电池盒作为航空电源系统主电源不工作或是失效时的直流后备电源,担负着地面启动涡轮发动机与空中应急点火的任务,同时还为机上一些重要用电设备供电.基于飞控蓄电池盒的重要性及针对存在的故障模式,采用故障寻址编码的技术对蓄电池进行故障诊断.充分研究了航空镉镍蓄电池盒的结构以及性能指标,设计一套自动飞控蓄电池盒故障检测系统,用来检测蓄电池盒的各性能参数.试验结果表明:测试设备能够提高检测精度,提前预测故障,提高蓄电池的性能品质和可靠性,满足飞机飞行的安全要求.     

基于机器学习的列车设备故障预测模型研究

决策树作为机器学习和数据挖掘领域中广泛应用的预测模型,其输出结果易于理解和解释。针对高速铁路车载智能设备数量庞大的流数据且设备故障复杂和诊断效率低等问题,采用CVFDT决策树算法,通过对规范化的列控设备流数据进行机器学习,构建车载设备智能故障预测模型（低概率发生、高概率发生和已发生故障）,实现对设备潜在故障“事前排除”,提高故障分类精度、定位和诊断准确性,保障高速铁路运营安全和运输效率。