过程参数滋扰报警的一类自适应管理策略

滋扰报警是过程工业报警系统的主要问题,然而,目前报警优化方法一般敏感度低、可靠性差,根据报警变量之间的时间间隔和报警持续时间确定滋扰报警的形式,提出了自适应报警死区和报警延时器的计算方法,利用时间序列ARMA模型预测报警死区来处理高频报警,并利用报警间隔时间更新报警延时器参数n来处理低频报警。通过工业实例数据验证表明,所提出的方法能够有效减少滋扰报警的数量、提高报警系统的性能。     

PVC生产中DCS系统报警优化改造

介绍了PVC生产中DCS系统工艺报警遇到的报警泛滥、滋扰报警、常驻报警等问题,提出了优化措施。     

过程报警合理化设置方法及其应用

通过定义报警设定点、响应时间及优先级确定方法等,详细介绍了过程报警合理化的设置方法。该方法在某厂工业碱性废水处理系统中的应用结果表明:报警设置合理,笔者所提方法具有可行性。     

多工况生产环境报警优化管理

以乙烯装置为例,分析经常切换使用设备和流程的生产工艺,对多工况生产环境下所产生的无效报警提出治理方案,并实现在CS3000系统中的下装,实现多工况报警抑制、报警参数一键切换及报警信息优化等功能,实现了不同生产工况下报警信息的整理、分析和优化,消除了无效报警,提高了生产操作的安全性,降低了操作人员的劳动负荷。     

基于NXPLPC1766及CAN总线的舰船机舱自动延伸报警系统研究

采用NXPLPC1766及CAN总线实现船舶机舱自动延伸报警系统能够更好的监控船舶机舱的运行状况及环境，使自动延伸报警更具稳定性及可靠性。软件设计采用模块化思想，实现了自我监控、设置运行参数及声光报警的多样式等功能，在船舶中应用，结构简单、功能完善，可以实现自动监控和自动延伸报警.     

仓储信息管理系统的设计与实现

为了解决传统的仓库管理存在的不及时性、不准确性以及繁琐、重复等问题,本文研究的就是基于信息技术及物联网技术搭建的仓储管理平台,通过单元仓库内建的智能指纹识别系统,智能视频监控系统,温湿度报警系统和智能联动报警系统的综合信息管理,实现对仓库物资的动态信息随时掌控,对异常信息及时报警,对数据系统化留查备案和历史数据分析。设计并实现了一种结合物联网技术的军用仓库智能管理系统,实现了系统管理,日常作业管理、巡库查库、库房物资管理、库房仓储动态和监控报警等主要功能。系统通过原始纸质单据和业务流程还原到系统,在设计上界面简洁,易操作,贴近用户使用习惯。系统经过全面调试和功能测试,完成了单元,过程和系统的集成测试。本文设计并实现的仓库管理系统实现了网络,无纸化,信息和智能。     

液氯钢瓶自动充装与标签管理系统

介绍一种液氯钢瓶自动充装与标签管理系统,给出系统特点与组成。采用先进的电子标签管理技术、无线网络通信及数据库技术等,实现了液氯钢瓶充装控制、监控报警和钢瓶智能信息化管理。     

iFIX组态软件监测系统在胶带运输系统的应用

介绍了iFIX组态软件检测系统的报警管理机制及其主要功能，分析iFIX监测反馈信息产生的条件及监测数据的加载和事件信息采集的整个过程。将iFIX自动化监控组态软件在胶带输送机系统上位机运行画面中关于胶带输送机的保护传感器开关量的事件状态变化进行显示或者报警，实现故障发生后第一时间提醒集控操作人员，而且在画面上以颜色、图标等有效直观的显示方法将发生的报警事件和发生过的报警事件显示出来，提高了生产运行过程中运输系统的可控性和安全性。     

流程工业报警系统传统评估方法分析及改进

流程工业报警系统通过报警信息显示当前过程存在的异常情况,提醒操作员及时干预以避免生产事故,其运行效率和流程工业的安全性能密切相关.报警系统效率低下会严重影响流程工业安全经济运行,然而报警系统优化与再设计的过程复杂,耗费大量人力物力,并且可能影响整个生产过程的正常运行.报警系统是否需要重新优化、何时优化、优化方向等问题困扰着工程研究人员,需要对其进行正确的评估.针对评价流程工业报警系统的常用定性指标进行机理分析,指出对于报警系统运行效率影响较大的几个因素,在此基础上构建了量化评估报警系统效率的指标,评估指标可以使工程研究人员更为直观地了解报警系统运行效率及其对于流程工业的影响,辅助优化报警系统.     

煤矿安全监控系统常见误报警原因分析及防治措施

煤矿安全监控系统存在传感器传输易受电磁干扰、各系统间联动性差、设备智能化程度低等方面的现实情况,通过提供煤矿安全监控系统的总体架构,对煤矿安全监控系统误报警产生的原因进行了全面、详细的分析和原因探讨,并对每部分的具体改造内容进行了一定的说明,提出一定的对策,确保煤矿安全监控系统的性能指标,对煤矿安全监控系统的稳定,防止误报警具有一定的推广应用价值。     

报警管理系统的优化实践

报警处理课题一直是近年关注的焦点。实施报警原则是改进报警现状的关键成功因素。可通过一系列已证明有效的工具及方法改进报警处理-例如报警值修改、报警搁置、报警掩蔽等。通过消除最频繁出现的前10名报警的活动,报警现象锐减,全厂报警次数下降了55%警优化行动取得了明显效果。     

Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

Alarmflood is one of themain problems in the alarmsystems of industrial process. Alarmroot-cause analysis and alarmprioritization are good for alarmflood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarmpriority and reduce the blindness of alarmhandling. As a case study, the Tennessee Eastman process is utilized to showthe effectiveness and validity of proposed approach. Alarmsystem performance comparison shows that our rationalization methodology can reduce the alarmflood to some extent and improve the performance.     

ALARM CHARACTERISTICS FOR A FLOOD WARNING SYSTEM WITH DETERMINISTIC COMPONENTS

Abstract. A method for evaluating a predictor-based alarm system is studied in this paper. The predictor is composed of a 'deterministic' component reflecting external information and a statistically based component for the error between the measurements and the external predictor. The aim of the predictor study is twofold:it is a means of interpreting the connections between the alarm and the catastrophe, and it can be used to select suitable alarm levels. As an application, the performance of a water-level predictor as part of a flood warning system has been evaluated. The result of this analysis shows that an alarm system which operates when the predictor reaches a certain level will tend to give either too many alarms or alarms that are out of phase with the catastrophe.     

An online method for detection and reduction of chattering alarms due to oscillation

Chattering alarms, which repeatedly and rapidly make transitions between alarm and normal states in a short time period, are the most common form of nuisance alarms that severely degrade the performance of alarm systems for industrial plants. One reason for chattering alarms is the presence of oscillation in process signals. The paper proposes an online method to promptly detect the chattering alarms due to oscillation and to effectively reduce the number of chattering alarms. In particular, a revised chattering index is proposed to quantify the level of chattering alarms; the discrete cosine transform-based method is used to detect the presence of oscillation; two mechanisms by adjusting the alarm trippoint and using a delay timer are exploited to reduce the number of chattering alarms. An industrial case study is provided to illustrate the effectiveness of the proposed method.     

A practical approach to improve alarm system performance: Application to power plant

Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal condition in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. However, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers(i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.     

一种新的模糊聚类-分级算法及其在流程报警管理中的应用

1 INTRODUCTION Normally, operating management experience of in- dustrial process indicates that the probability one ig- nores alarm is 10-4 after handling one alarm signal and the probability one takes no direct action is 10-5 after hearing one alarm. Experiences also indicate that the probability one ignores alarm will increase to 10-3 from 10-4 if one alarm isn't handled within one minute[1]. Therefore, when the second alarm is appearing the probability which it can't be identified is 10…     

Quantification of alarm chatter based on run length distributions

In the process industry, alarms are configured on the control system to provide indication of abnormal events to the control room operators. In the presence of improper design of alarm generating algorithm or lack of appropriate tuning, alarms are announced more frequently than what is typically sufficient to alert the operator, a condition commonly known as ‘alarm chatter’. Chattering alarms are the most common form of nuisance alarms. The concept of run length is introduced in the alarm management context to study alarm chatter and an index is proposed to quantify the degree of alarm chatter based on run length distributions obtained exclusively from readily available historical alarm data. Chatter index hence plays a crucial role in routine assessment of industrial alarm systems. Prominent features of the proposed chatter index and its variant are demonstrated using industrial data.     

非规整报警的相关性分析方法

报警管理在化工过程安全中具有重要地位,而报警相关性挖掘是报警管理的重要组成部分。在传统的基于凝聚层次聚类的报警相关性挖掘方法基础上,提出非规整报警相关性计算方法,弥补了传统方法难以处理延迟时间变化与非对称情况的不足。同时以概率形式表示相关性大小,使不同位点之间的相关性计算结果具有可比性。经过仿真案例与工厂真实生产案例测试,该方法能够有效挖掘过程中出现的报警相关性,进而指导报警系统合 理化。     

Dynamic alarm prediction for critical alarms using a probabilistic model

Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper, we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.