工业报警序列的模糊关联规则挖掘方法

面向寻找工业报警序列根源,抑制报警泛滥,论文提出了一种模糊加权关联规则挖掘方法,结合模糊集合、Apriori数据挖掘算法和时间序列分析挖掘报警关联规则。基于报警数据的时间约束属性和相似度约束属性,利用相似度作为模糊加权关联规则挖掘算法的权重,提高挖掘效率和准确性。并且,相对于定量表达,模糊关联规则对于操作者来说更加容易使用。工业实例验证了方法的有效性。     

基于矩特征傅里叶描述的示功图故障诊断研究

提出一种结合矩特征和傅里叶描述子的示功图故障诊断方法。示功图经过最佳阈值分割和边缘检测后,得到边界为最大区域填充后的图像,计算图像的矩特征获得表示物体形状的矩特征序列,再通过离散傅里叶变换得到具有平移、旋转和尺度不变性的归一化矩特征傅里叶描述子,采用欧氏距离法进行分类。实际应用表明:该诊断方法在油田抽油机故障诊断中具有可行性和有效性。     

基于傅里叶描述子的示功图诊断方法研究

提出了基于傅里叶描述子提取轮廓线特征的示功图故障诊断方法。利用八邻域搜索和形态学方法提取示功图轮廓线,在此基础上计算轮廓线边界点序列的归一化傅里叶描述子,保证了图像对于旋转、缩放和平移的不变性,并将其作为特征量输入到距离分类器中,从而实现故障智能诊断。实验结果表明,该方法可以有效地识别出示功图的不同故障类型。     

过程工业报警系统可视化监控技术及应用

合理有效的可视化监控技术及工具有助于操作员及时理解报警信息并采取响应措施。针对现有可视化技术存在的缺点及不足, 如资源利用不充分、报警等级划分不明确、报警根源分析不彻底等, 构建了4种新型可视化工具:基于信息融合的解释结构模型(静态和动态)、层次高密度报警图、层次优先级色彩图、性能水平趋势图, 分别实现了过程递阶模型建立、报警根源分析、滋扰报警识别、报警优先级划分、报警系统性能常规评估等目的。以TE仿真模型为例, 阐明了上述可视化技术及工具的实用性和有效性, 不仅可以展示报警全貌原始信息, 还可快速识别报警根源、关键报警、滋扰报警以及报警系统性能水平, 实现了高效监控,从一定程度上解决了报警泛滥问题。     

广西产毛果算盘子不同部位HPLC指纹图谱研究

采用HPLC研究广西不同产地毛果算盘子不同部位的指纹图谱，共分析了5批毛果算盘子药材根茎叶3个部位的HPLC指纹图谱，建立了3个部位的指纹图谱，且相似度较好。根据相似度分析确定了毛果算盘子药材根部位共有10个、茎部位共有16个和叶部位共有13个指纹图谱的特征峰。所建方法简单、稳定、重复性好，可用于毛果算盘子药材的质量控制。     

红外光谱结合聚类分析对罗汉松实进行鉴别

采用傅里叶红外光谱法对十批不同产地的罗汉松实进行分析,并采用OMNIC8.0红外软件和SPSS19.0软件处理和分析实验数据,从而建立罗汉松实一种符合自身特点的鉴别分析方法,为罗汉松实的鉴别和质量评价提供实验基础。通过对不同产地的罗汉松果实的红外光谱数据进行分析,获得了罗汉松实红外指纹图谱,并进行相似度计算和聚类分析。本实验所采用的技术及建立的方法简单、快捷准确、耗时短,可为罗汉松实定性鉴别的研究以及进一步的开发和利用提供科学的参考依据。     

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。     

聚氨酯丙烯酸油漆固化度定量测定研究

主要采用傅里叶红外光谱定量测定聚氨酯丙烯酸油漆的固化度,分析固化时间对其固化度的影响,同时研究在不同固化度条件下油漆物理性能和断面形貌,由此确定最优固化工艺条件及最佳固化度范围。研究表明:随着UV(紫外线)能量的增大,固化度的变化差值先增大后减小,根据固化前后特征峰和参比峰的数值定量计算固化度值,同时通过测定固化油漆的物理性能发现其最佳固化度范围≥60。     

复合PLS模型在近红外光谱分析煤炭中的应用

为了更好地确定偏最小二乘法模型的主成分数,提出一种传统偏最小二乘法和多主成分数偏最小二乘法相结合构建复合偏最小二乘模型的方法。给出了预测时两种样品相似度的计算方式:直接距离法和性质得分距离法。分别采用复合偏最小二乘法和传统偏最小二乘法对煤炭的全硫、灰分、热值和碳含量进行建模预测,比较传统偏最小二乘法和多主成分数偏最小二乘法建模过程中的相关系数和交互验证均方根误差,采用复合偏最小二乘模型对验证集样品预测时,计算了不同相似度计算方式下不同样品间距离算法的预测均方根误差,并同传统偏最小二乘法预测均方根的误差进行比较,结果表明:复合偏最小二乘法建模比传统偏最小二乘法建模有更强的适应性,能够提高预测的准确性。     

重量法检测污水中悬浮物含量的不确定度评定

采用重量法对4个不同浓度的实际污水水样的悬浮物含量进行了检测,并根据检测结果进行了测量不确定度的评定。根据实验过程,建立了数学模型,对各个不确定度的来源进行了分析,并计算出各个不同浓度样品的不确定度分量、合成不确定度和扩展不确定度,找到了占支配地位的不确定度分量,从而使检测人员对该方法检测悬浮物含量进行不确定度评定的计算变得简单实用。     

Pattern matching of alarm flood sequences by a modified Smith–Waterman algorithm

Alarm flooding is one of the main problems in alarm management. Alarm flood pattern analysis is helpful for root cause analysis of historical floods and for incoming flood prediction. This paper deals with a data driven method for alarm flood pattern matching. An alarm flood is represented by a time-stamped alarm sequence. A modified Smith–Waterman algorithm considering the time stamp information is proposed to calculate a similarity index of alarm floods. The effectiveness of the algorithm is validated by a case study on actual chemical process alarm data.     

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.     

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。     

现代化工流程报警系统分析及管理策略

分析了现代石化流程报警系统现状,结合现代化工流程报警的特点,提出了适合于化工流程的合理设置报警系统的管理方法与策略,提出的报警管理思想对于现代化工流程报警系统的有效管理具有较强的实用性。     

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.     

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.     

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.     

Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis (LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis (PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.