石化行业自动化控制仪表常见故障分析及其处理

介绍化工生产过程中仪表故障及其处理措施。     

过程检测仪表防腐技术的研究

在工业生产中一些腐蚀介质给过程检测仪表的应用带来了许多困难,为了满足生产需要,针对腐蚀问题要采取一些技术措施.该文详细叙述了工业过程检测仪表腐蚀的种类、腐蚀原理,针对各种仪表采用金属材料的耐腐蚀性能的不同而采取不同的防腐措施,对工业企业防腐仪表的设计及选型具有一定的参考作用.     

Novell LAN常规故障分析与排除

本文结合我校计算中心ＮｏｖｅｌｌＬＡＮ的具体实例，对ＮｏｖｅｌｌＬＡＮ使用正程中常见故障进行分析，并提出相应的排除方法。     

自动化仪表的日常维护及常见故障分析

分析自动化仪表常见问题出现的原因,介绍维护措施,并提出注意事项.     

现场仪表常见故障分析

介绍化工生产中的温度、压力、流量、液位控制系统故障分析,总结仪表现场维护经验.     

硬盘驱动器故障分析与排除

硬盘驱动器故障分析与排除甘肃省计算中心（兰州７３００３０）田立强，张正权本文试图通过对一例硬盘驱动器的维修过程，浅谈对一般硬盘驱动器的检修过程与维修方法。本例的故障盘型号为ＳＴ－２２５。故障现象：每次开机后屏幕左上角出现“１７０１”错误信息，并提示按...     

世纪之交的自动化仪表--检测仪表与执行器的技术进展

概要介绍了世纪之交人们关注的自动化仪表，重点是流量、物位检测仪表与执行器的技术进展。     

微机故障分析及排除六例

微机故障分析及排除六例李村合，崔培伟（石油大学计算机系山东东营２５７０６２）例一：故障现象：一台浪潮３８６ＤＸ／４０计算机，因用户缺乏经验，带电拔插键盘插头，再开机后，自检时出现３０１错误信号，按键后均有反应，但按键与显示不符。故障分析及排除；３０１...     

安全栅一例故障分析

随着各行各业生产建设的发展,DDE-Ⅲ型电动单元组合仪表的使用日益广泛。下面指出笔者在使用过程中遇到的一个少见的问题,以引起制造厂的注意。某厂生产的DFA-1200型安全栅,故障现象是:按使用说明书校验1～5V.DC输出电压正常,按图1所示校验4～20mA.DC输出电流也正常,但使用中将图1中电流表改为数字显示仪表后,其系统工作则不正常,当满量量程时安全栅输     

靖边气田净化厂自控仪表回路常见故障分析

净化厂在生产过程中经常出现仪表故障现象,由于检测与控制过程中出现的故障现象比较复杂,正确判断、及时处理生产过程中仪表故障,不但直接关系到生产的安全与稳定,同时,也涉及到产品的质量和消耗。现阶段自动化水平的不断提高,对现场仪表维护人员的技术水平提出了更高要求,要随时对生产过程中使用的仪表进行维护并能对常见故障及时处理。本文根据第三净化厂检测仪表常见的故障,对仪表维护及运行中出现的故障现象进行分析与处理的分别阐述,总结出故障解决方法的具体思路。     

保险杠抗压分析流程自动化系统开发

为提升汽车保险杠抗压分析效率、减少重复性劳动、实现仿真分析流程的自动化,利用Python和VBS语言开发一套前、后处理自动化程序。此程序可实现RBE3单元创建、局部坐标系创建、载荷创建、头文件创建、后处理、结果诊断和报告编制等过程的自动化。实际应用结果表明,与传统人工分析相比,该程序可使仿真分析效率大幅提高。通过自动化程序进行仿真,不仅可以保证保险杠抗压分析的精度,同时还可以实现分析的标准化、保证分析的一致性。     

Variable-weighted Fisher discriminant analysis for process fault diagnosis

Variable-weighted Fisher discriminant analysis (VW-FDA) is proposed to improve the fault diagnosis performance of the conventional FDA. VW-FDA incorporates the variable weighting into FDA. The variable weighting is used to find out each weight vector for all faults. After all fault data are weighted by the corresponding weight vectors, the summed fault data can be constructed to magnify each fault’s local characteristics. Then, VW-FDA is performed on the summed fault data rather than the original fault data. It is helpful to extract discriminative features from overlapping fault data. Moreover, the partial F-values with the cumulative percent variation are used for exactly variable weighting, which is indispensable to VW-FDA. The proposed approach is applied to Tennessee Eastman process. The results demonstrate that VW-FDA shows better fault diagnosis performance than the conventional FDA.     

Measuring process control automation benefits through a human-machine function allocation analysis

Often technology is introduced into manufacturing and process control environments to partially automate tasks too complex to fully automate. However, it is not always clear how to measure the benefits of such projects. More often than not, automation eliminates but also shifts human work, and broad productivity measures can fail to capture such changes. To this end, this effort describes a manufacturing case study that looks at human-machine allocation metrics involving a laboratory fermentation unit upgrade. Using a workflow monitoring and function allocation analytic approach, it was determined that upgrading an older microbial culture bioreactor (fermentor) to a new design with intelligent monitoring capabilities resulted in an approximate 17% reduction in dedicated human supervision. This workload reduction allowed scientists to spend less time on repetitive tasks and more time concentrating on other, more open-ended problems that require more expertize. However, the new technology increased human efforts across other functions, suggesting potential mitigation paths for future technology development. This effort illustrates that the impact of new technology on human-machine tasking can be quantified through a function allocation analysis and also provide diagnostic information, both of which are critical in understanding any overall added benefit of intelligent systems.     

The process chemometrics approach to process monitoring and fault detection

Chemometrics, the application of mathematical and statistical methods to the analysis of chemical data, is finding ever widening applications in the chemical process environment. This article reviews the chemometrics approach to chemical process monitoring and fault detection. These approaches rely on the formation of a mathematical/statistical model that is based on historical process data. New process data can then be compared with models of normal operation in order to detect a change in the system. Typical modelling approaches rely on principal components analysis, partial least squares and a variety of other chemometric methods. Applications where the ordered nature of the data is taken into account explicitly are also beginning to see use. This article reviews the state-of-the-art of process chemometrics and current trends in research and applications.     

Non-linear principal components analysis with application to process fault detection

Principal component analysis has been used for the development of process performance monitoring schemes for both continuous and batch industrial processes. However, it is a linear technique and in this respect it is not necessarily the most appropriate methodology for handling industrial problems which exhibit nonlinear behaviour. A nonlinear principal component analysis methodology based upon the input-training neural network is proposed for the development of nonlinear process performance monitoring schemes. Kernel density estimation is then used to define the action and warning limits, and a differential contribution plot is derived which is capable of identifying the potential source of process faults in nonlinear situations. Finally, the methodology is evaluated through the development of a process performance monitoring scheme for an industrial fluidized bed reactor.     

基于时序过程片段分析的符号有向图实时故障诊断方法

本文在传统符号有向图(SDG)方法的基础上,在SDG模型的节点都引入趋势基元思想,按时序过程顺序对化工生产过程数据进行实时趋势分析.将趋势明显、符合SDG方法处理的故障点进行寻源报警处理;将由生产调整、误差等引起的尖峰现象从故障检测中剔除;同时将实时数据运用绝对值差分累积和计算,对缓变故障和波动大、不稳定的变量有预防和警示作用.该系统方法使故障检测在运用SDG模型的因果关系的同时,更加有效地利用历史数据趋势.经过某石化厂PTA装置溶剂脱水塔实例分析,该方法使SDG的溯源范围更加有效,可避免干扰造成的系统扰动引起的误报,减少其多义性.     

改进的核费舍判别分析法应用于故障诊断

化工过程采样数据具有强非线性和噪声,针对化工过程状态监控的问题,提出一种改进的核费舍判别分析法（KFDA）的故障诊断算法。首先采样数据经过小波变换方法去除噪声,去除噪声后的数据进行KFDA建模,然后在建模同时采用特征向量选择（FVS）算法降低复杂性。Tennessee Eastman process实验结果表明了该算法的有效性,同时该算法加强了KFDA故障诊断的准确性,并明显地减少了存储空间和运算时间。     

基于ICA-PCA的化工流程仪表故障诊断

传统的多元统计过程控制(MSPC)的故障诊断方法要求观测变量数据服从高斯分布,然而实际化工流程中的仪表数据中难以满足这一要求。针对这一问题,提出在仪表数据中提取分离出非高斯信息和高斯信息,并分别利用独立元分析法和主元分析法建立不同的故障诊断模型。在检测到发生故障后,通过改进的贡献度算法定位出发生故障的仪表。通过对Tennessee Eastman(TE)过程数据进行仿真研究,验证了ICA-PCA故障诊断法在化工流程仪表不同故障诊断中的有效性。     

基于神经网络的化工过程故障诊断

由于化工过程的高复杂性及高危险性,且装置都是都是长周期连续运转,一旦出现故障会造成巨大的损失,因此对化工过程和设备进行早期和准确的故障检测与诊断,可以提高设备运行的安全性,避免发生重大安全事故,降低生产成本.人工神经网络具有非线性、大规模、并行处理能力强,以及鲁棒性、容错性、自学习能力强等特点,处理化工过程的复杂非线性问题,比其他方法都优越.本文描述了人工神经网络的基本原理,及近年来人工神经网络在化工过程故障诊断应用中的进展.以BP神经网络为例,分析和介绍了其结构和学习算法,说明了神经网络故障诊断的推理过程,并建议将神经网络与符号有向图(SDG)结合诊断故障.