共查询到16条相似文献,搜索用时 78 毫秒
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通过分别导出T2和SPE统计量均值与过程数据统计参数之间的关系,分析了T2和SPE统计量的变化趋势以及与密闭鼓风炉实际生产状况的对应关系;基于现场采集的长期历史数据,给出了在密闭鼓风炉过程传感器故障检测中的应用实例;试验结果表明,PCA方法可以快速有效地反映生产过程的变化,生产运用效果表明该方法大大提高了对密闭鼓风炉生产工况的实时监测能力,提高了生产效率. 相似文献
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从粗糙集等价类概念出发,提出从不完整数据集中获取故障诊断知识的密闭鼓风炉故障诊断方法,将不完整数据集的训练事例划分为下近似和上近似两类,首先假设属性的未知特征值为任意可能值,然后根据从驯练事例中得到的上下近似进行提炼,最后从事例与近似互相作用以推导出确定的和可能的规则,得出规则概率,并估计出合适的属性的未知特征值,结合密闭鼓风炉悬料规则库的知识获取及其在故障诊断中的应用过程说明了该方法的有效性和实用性。 相似文献
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介绍韶关冶炼厂Ⅱ系统铅锌密闭鼓风炉熔炼(ISP)生产过程DCS(集散系统)的应用情况。着重论述了系统的监控功能和技术特征。 相似文献
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密闭鼓风炉的混合故障诊断方法 总被引:3,自引:0,他引:3
以密闭鼓风炉为研究对象,在利用小波分析进行数据预处理的基础上,将专家系统、模糊控制、神经网络等先进的理论和技术有机地结合起来,提出了模块化的混合故障诊断系统,同时给出了系统框图,并详细说明了各组成部分的结构和功能,从而实现了对整个密闭鼓风炉生产系统的状态监测和故障诊断,避免了对炉况的漏判或误判。 相似文献
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概率主元分析(PPCA)已广泛应用于工业过程监测.然而,PPCA法仅构造了生产过程的静态线性关系,处理具有较强动态特性的实际工业生产过程效果较差.为此提出动态概率主元分析(DPPCA)法,对经过时谱扩展后的变量数据阵,通过期望最大化(EM)算法建立生成模型,从而将静态PPCA推广到动态多变量过程.最后将此法应用于TE过程的仿真研究,结果表明该法有效. 相似文献
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针对密闭鼓风炉故障信息的复杂性和不完备性,建立了基于粗糙集(RS)和最小二乘支持向量机(LS_SVM)相结合的故障诊断模型。首先运用等频率划分法对故障诊断数据中的连续属性进行离散化,然后采用粗糙集理论进行故障诊断决策系统约简,获得最优决策系统。将约简结果与LS_SVM相结合,建立了故障诊断模型。实验结果表明,该模型提高了诊断效率和判断准确率。 相似文献
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改进PCA在发酵过程监测与故障诊断中的应用 总被引:6,自引:0,他引:6
提出一种改进的主元分析(PCA)法.利用主元相关变量残差统计量代替平方预测误差Q统计量,并采用累积方差贡献率及复相关系数确定PCA模型的主元数.将改进的主元分析法应用于粘菌素发酵过程监测和故障诊断中,仿真结果表明改进的PCA方法避免了Q统计量的保守性,并保证了主元子空间中的信忠存量.与一种基于特征子空间的系统性能监控方法相比较,改进的PCA方法具有更强的有效性. 相似文献
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Incidents happening in the blast furnace will strongly affect the stability and smoothness of the iron-making process. Thus far, diagnosis of abnormalities in furnaces still mainly relies on the personal experiences of individual workers in many iron works. In this paper, principal component analysis (PCA)-based algorithms are developed to monitor the iron-making process and achieve early abnormality detection. Because the process exhibits a non-normal distribution and a time-varying nature in the measurement data, a static convex hull-based PCA algorithm (SCHPCA) which replaces the traditional T2-based abnormality detection logic with the convex hull-based abnormality detection logic, and its moving window version, called the moving window convex hull-based PCA algorithm (MWCHPCA) are proposed, respectively. These two algorithms are tested on the real process data to verify their effectiveness in the early abnormality detection of iron-making process. 相似文献
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基于SPSS与BP网络的锌产量预测模型 总被引:1,自引:0,他引:1
金属锌对人们的生活具有非常重要的意义,为了使锌产量最大化,有必要对密闭鼓风炉铅锌熔炼操作参数进行优化,提出了基于SPSS与BP网络的密闭鼓风炉熔炼锌产量的预测模型;先对DCS系统上获得的在线数据建立初步的参变量与锌产量之间的因果关系,再利用SPSS统计分析软件中的主成分分析法对各参变量进行分析,最后将得到的与锌产量最相关的少数几个因素用BP网络建立预测模型,仿真结果表明,该方法减少了网络的训练时间,改善了学习效率,具有较高的预测精度,是可行的、有效的。 相似文献
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Recursive PCA for adaptive process monitoring 总被引:3,自引:0,他引:3
While principal component analysis (PCA) has found wide application in process monitoring, slow and normal process changes often occur in real processes, which lead to false alarms for a fixed-model monitoring approach. In this paper, we propose two recursive PCA algorithms for adaptive process monitoring. The paper starts with an efficient approach to updating the correlation matrix recursively. The algorithms, using rank-one modification and Lanczos tridiagonalization, are then proposed and their computational complexity is compared. The number of principal components and the confidence limits for process monitoring are also determined recursively. A complete adaptive monitoring algorithm that addresses the issues of missing values and outlines is presented. Finally, the proposed algorithms are applied to a rapid thermal annealing process in semiconductor processing for adaptive monitoring. 相似文献
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多元统计过程监控与安全生产 总被引:2,自引:0,他引:2
统计过程控制是一种改善产品质量及保证安全生产的有力工具。针对现有多元统计监控技术大多假定所考察的生产过程本身仅存在一个标准运行条件,导致实际应用时往往引发大量的连续报警的问题,本文基于主角度建立了任意两个主元模型相似性的度量,提出了一种基于多主元模型的过程监控方法。通过该方法能有效地检测、诊断工业过程中的异常,以避免事故的发生,将带来巨大的经济效益。最后,讨论了相应的软件实现平台EZMon及其应用。 相似文献
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Owing to various manufacturing strategies and demands of markets, chemical processes often involve multiple operating modes. How to identify mode from multimode process data collected under both stable and transitional modes is an important issue. This paper proposes a novel mode identification algorithm-recursive local outlier factor (RLOF) based on the sequential information in the time scale and the density information in the spatial scale. In this algorithm, not only the number of modes does not need to be determined in advance, but also details of mode switching can be acquired. In addition, the principal components (PCs) chosen by the variance of overall dataset in principal component analysis (PCA) cannot guarantee that all variables express information as completely as possible. Using the defined cumulative percent expression (CPE), this study chooses key PCs (KPCs) according to each variable. Moreover, fault diagnosis is realized via the contribution of every variable to key PCs. Finally, the monitoring performance is evaluated under the Tennessee Eastman (TE) benchmark and the continuous stirred tank reactor (CSTR) process. 相似文献