基于DeltaV的实时故障诊断系统应用与研究

为了使丁二烯生产装置能更安全高效的运行,解决DeltaV自带报警系统存在的不足,基于DeltaV设计了一个故障诊断系统.系统应用专家知识提取出了32个重要监测点,采用数据库技术建立专家知识库,应用OPC技术解决数据通信问题,使用VB编程设计了一个运行在上位机的基于DeltaV的故障诊断系统,并实现了操作员站上的实时故障报警.该系统具有良好的可扩充性.该系统现已投入实际运行,较好地满足了系统安全生产的需求.

一类连续反应的SDG HAZOP与故障诊断

在分析符号有向图(SDG)技术的理论基础上,探讨了SDG定性模型在计算机辅助HAZOP(危险与可操作性分析)和故障诊断中的实际应用,并针对该丙烯聚合反应(溶剂淤浆法)建立SDG模型,利用SDG技术进行HAZOP和故障诊断分析。实验结果表明,基于SDG的HAZOP和故障诊断技术具有完备性好、节省时间、人力、费用等众多优点。

MMC-HVDC子模块故障诊断与就地保护策略

子模块故障是模块化多电平换流器高压直流输电(modular multi-level converter based HVDC,MMC-HVDC)常见故障之一,其迅速及时的诊断及可靠的就地保护是保证系统稳定运行的重要条件。针对MMC-HVDC的子模块故障诊断与保护问题,分析了子模块故障特性,基于故障特性分析,根据每个控制周期内电容电压增量的理论值和实际值,提出一种子模块故障诊断判据来实现电容短路失效、IGBT开路以及桥臂直通短路等故障的诊断;提出一种排除法实现对续流二极管、电容开路失效或子模块内连接线开路等故障的诊断和定位。设计了冗余子模块"热备用"运行时的子模块就地保护策略。基于PSCAD/EMTDC搭建了双端21电平MMC-HVDC系统,仿真结果表明,所提出的策略能快速实现子模块故障的诊断并进行可靠的就地保护,保证系统的稳定运行。

模拟电路故障诊断的方法研究

本文主要研究了增加动量项BP神经网络算法,与典型的模拟电路系统相结合,设计出能够实现在线和离线诊断模拟电路故障的小型智能诊断系统,实现了智能算法在实际工业生产当中应用,实验证明,采用理论上成熟的动量项BP神经网络算法用于故障诊断,利用嵌入式系统应用性强、高可靠性、低功耗等特点,并且有一定的实用价值。

高速铁路箱梁提运架设备典型电控故障的诊断与维修

本文主要对高铁客运专线箱梁提运架施工特种设备的电控系统施工中出现的故障情况进行分析总结．对几种常见的典型故障的诊断与维修提出了相应的观点，可为后续施工提供参考。

智能数控机床远程诊断系统的设计

数控机床的构造越来越复杂,机床出故障的几率也越来越大,对一般技术人员来说维修越来越困难,这就限制了数控机床的使用,由于故障得不到及时的修复这必定给用户造成一定的经济损失,降低生产率,严重故障还会造成人员伤亡。根据数控机床的结构与功能特点,以数控机床为对象,通过对数控机床的运行特性进行了分析,进而对数控机床智能状态监测与故障诊断系统的一些关键技术进行详细的研究。首先对数控机床经常发生的可能性故障进行分析,基于Internet构建了一个数控机床远程协作诊断试验平台。对远程故障数据采集及信号处理设计了新的功能模块,通过各种传感器实时采集数控机床运行过程中的特征参数,并对特征参数进行实时分析与处理,利用专家系统对数控机床的故障发生进行辩识,建立了数控故障诊断知识的复合框架表示方法,为故障诊断系统提供信息、资源。

导弹武器装备综合测试诊断系统体系结构研究及其应用

提出了一种在体系结构上具备开放性、可移植性、软件重用性、仪器互换性、通信接口标准化的装备综合测试诊断系统;在此基础上研究了开放式测试系统的逻辑结构和故障诊断模块与测试系统的互连,另外,给出了基于测试引擎的综合测试诊断系统的软件设计层次结构,简要论述了测试引擎的执行模式;利用此方案简要说明了某武器系统综合测试诊断设备的设计与实现。

汽轮发电机转子绕组匝间短路故障研究

对汽轮发电机发生转子绕组匝问短路故障后,电磁特性和电气参量的变化进行了分析。发现了故障发生后励磁电流增加而无功输出却相对减少的特征,提出了利用故障前后励磁电流的计算值和测量值之间的相对变化率作为识别故障严重程度的一个实用的方法,建立了在线识别转子匝间短路的故障判据。该判据利用发电机转子匝间短路的动态模拟试验机组进行了相关的实验验证。

高压电磁式互感器故障特征与判据

随着高压电磁式互感器可靠性研究的发展与深化．互感器故障现象与故障机理显得越来越复杂和不确定．现用系统论的观点和方法概括了互感器故障的特征，总结了现有故障判据，进而提出了故障的模糊矩阵判据，为故障诊断的计算机化和实时化提供了基础．

高效测定模拟电路故障的子网络分解法

本文提出了一种模拟电路故障诊断的有效方法。把电路分解成一些子网络和一些适当的无源连接支路,按照含互感和四类受控电源网络的节点电压矩阵方程并根据在激励下对该电路可测节点电压的测量,推导出了故障诊断(FD)方程组与辅助判别式及其简化实用形式。某一故障子网络或者故障元件可以由这些方程直接测定。本文介绍的方法的有效性用一无源直流电路说明,其主要特性是使用其他方法难于兼备的。

Fault diagnosis method of AC motor rolling bearing based on heterogeneous data fusion of current and infrared image

In order to improve the accuracy of rolling bearing fault diagnosis when the motor is running under non-stationary conditions,an AC motor rolling bearing fault diagnosis method was proposed based on heterogeneous data fusion of current and infrared images.Firstly,VMD was used to decompose the motor current signal and extract the low-frequency component of the bearing fault signal.On this basis,the current signal was transformed into a two-dimensional graph suitable for convolutional neural network,and the data set was classified by convolutional neural network and softmax classifier.Secondly,the infrared image was segmented and the fault features were extracted,so as to calculate the similarity with the infrared image of the fault bearing in the library,and further the sigmod classifier was used to classify the data.Finally,a decision-level fusion method was introduced to fuse the current signal with the infrared image signal diagnosis result according to the weight,and the motor bearing fault diagnosis result was obtained.Through experimental verification,the proposed fault diagnosis method could be used for the fault diagnosis of motor bearing outer ring under the condition of load variation,and the accuracy of fault diagnosis can reach 98.85%.

Rolling bearing fault diagnosis based on data-level and feature-level information fusion

To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed.First,according to the impact characteristics of rolling bearing faults,correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals.These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals.Subsequently,a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings.Finally,the fused features were fed into a Softmax classifier to complete the fault diagnosis.The results show that the proposed method exhibits an average test accuracy of over 99.00%on the two rolling bearing fault datasets,outperforming other comparison methods.Thus,the method can be effectively utilized for diagnosing rolling bearing faults.

别克荣御系列车外后视镜的原理与检测

说明了别克荣御系列车外后视镜的控制原理,介绍了其使用方法,说明了其功能检查及故障诊断检测方法。

Nonlinear online process monitoring and fault diagnosis of condenser based on kernel PCA plus FDA

A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.

Fault isolation of air handling unit based on neural network

Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation results show that the measurement faults of the supply air temperature can lead to the increase in energy consumption.According to the fault characteristics,a data-driven method based on a neural network is presented to detect and diagnose the faults of air handling units.First,the historical data are selected to train the neural network so that it can recognize and predict the operation of the system.Then,the faults can be diagnosed by calculating the relative errors denoting the difference between the measuring values and the prediction outputs.Finally,the fault diagnosis strategy using the neural network is validated by using a simulator based on the TRNSYS platform.The results show that the neural network can diagnose different faults of the temperature,the flow rate and the pressure sensors in the air conditioning system.

Development of Fault Diagnosis System for Spacecraft Based on Fault Tree and G2

Some ideas in the development of fault diagnosis system for spacecraft are introduced. Firstly, the architecture of spacecraft fault diagnosis is proposed hierarchically with four diagnosis frames, i.e., system level, subsystem level, component level and element level. Secondly, a hierarchical diagnosis model is expressed with four layers, i.e., sensors layer, function layer, behavior layer and structure layer. These layers are used to work together to accomplish the fault alarm, diagnosis and localization. Thirdly, a fault-tree-oriented hybrid knowledge representation based on frame and generalized rule and its relevant reasoning strategy is put forward. Finally, a diagnosis case for spacecraft power system is exemplified combining the above with a powerful expert system development tool G2.

Development of Management Systems for Electric Vehicle Battery Series

A kind of management system for electric vehicle (EV) battery series was developed. The system can predict residual capacity for EV battery series and mileages. The system can determine if it is necessary for the battery series to be charged. The system can determine which battery is necessary to be updated for the reason of damage or aging. The system can display the total voltage of battery series, extreme voltage and temperature of every battery in the series. The system can display the accumulative discharge for every battery in the series. The system can alarm when both total or extreme voltage is at low level, or temperature of a battery in the series is at high level. The system provided with a microprocessor as key part can collect and record signal of charging and discharging current, total voltage, extreme voltage and temperature for every battery. The mathematical model of residual capacity for EV lead acid batteries was discussed in details. The system operates well in the laboratory and meets the requirement.