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contribution plot
相关语句
  贡献图
     Three monitoring statistics T~2_r,T~2_s and Q are built for fault detection. Contribution plot is used to isolate faulty variables and locate fault source.
     3个监测量(Tr2,T2s,Q)用来进行故障检测,同时使用贡献图分离故障变量,并判断故障原因.
短句来源
     A contribution plot method was proposed to isolate the faulty variables.
     在此基础上,提出了一种贡献图方法.计算过程变量对故障的贡献量,用于故障变量的分离。
短句来源
     Using Exponential Weighted Moving Covariance Plot (EWMA Plot) and MSPC Control Plot (such as SPE-score picture, T2 picture and Principal Component Contribution Plot, etc) to monitoring procedure process, and to examine abnormal conditions in process.
     利用指数加权移动平均图(EWMA图)和多变量统计控制图(SPE-score图、T2图和主元贡献图等)方法对生产过程实行监控,检测生产过程异常情况。
短句来源
     The simulation results on TE process show that the proposed method can detect process faults earlier than traditional PCA and dynamic PCA ( DPCA) , and contribution plot can indicate faulty variables exactly.
     在TE过程上的监控结果表明,MSKPCA可以比PCA和动态PCA更迅速地检测到过程故障,贡献图方法能够正确地分离故障变量。
短句来源
  相似匹配句对
     adjustment of plot.
     情节的调整。
短句来源
     The Contribution of Qingbao
     清宝的投稿
短句来源
     On “Relation Contribution
     论“关系稿”
短句来源
     A contribution plot method was proposed to isolate the faulty variables.
     在此基础上,提出了一种贡献图方法.计算过程变量对故障的贡献量,用于故障变量的分离。
短句来源
     News Plot
     新闻策划之我见
短句来源
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  contribution plot
With this same model applied to a second chamber, again a contribution plot can be used to identify the cause of the single point excursions.
      
The illustrations below show a Pareto graph and a multi-objective contribution plot showing the Pareto points for three objectives.
      
In contrast, the contribution plot will indicate the local deviation in a particular run.
      


A major technical challenge facing the manufacturing and process control industries is the need to improve production consistency and provide early warning of process faults and malfunctions. MSPC(Multivariate Statistical Process Control) based on the techniques of PCA(Principal Components Analysis) provides tools for on-line performance monitoring. PCA generate reduced dimension data set according to the original data with fewer number of variables while still maintain the capability of describing the embedded...

A major technical challenge facing the manufacturing and process control industries is the need to improve production consistency and provide early warning of process faults and malfunctions. MSPC(Multivariate Statistical Process Control) based on the techniques of PCA(Principal Components Analysis) provides tools for on-line performance monitoring. PCA generate reduced dimension data set according to the original data with fewer number of variables while still maintain the capability of describing the embedded variation causes. Several techniques that support the PCA process control model such as Hotelling's, SPE(Squared Prediction Error) and combined fault detection indices are adopted to identify if data has fault information; in addition, fault reconstruction via optimization and contribution plots methods play roles in finding the causes or locations of the faulty data. Computer applications implemented by LabVIEW with interactive functionalities and graphical tools are illustrated.

如何提高生产连续性和提前提供过程故障警告,是当今工业工程和过程控制领域面临的一项重要挑战。建立在主元法(PCA:PrincipalComponentsAnalysis)基础上的多元统计过程控制,为在线过程监控提供了有利的工具。一些方法例如Hotelling的方法,SPE(SquaredPredictionError),以及综合指数法,被用来识别存在故障的数据;另外通过最优化方法来完成重构,再利用重构法对过程故障进行识别和监控的方法,以及贡献图方法,对找出故障的位置和原因更起到了不可或缺的作用。通过列举拥有完善的人机互动功能及图表表示法的LabVIEW程序对上述理论的实现,更直观地了解并应用了以重构法来实现监控故障。

Principle Component Analysis (PCA) is an effective way not only to eliminate correlation among process variables and reduce the influence of noise and disturbance on system, but also to reserve enough information of original data characteristics needed for modeling a industrial complex process. Based on principle component model, detection and diagnosis analysis is carried out on a typical Heavy Oil Fractionator with multivariate statistical techniques such as Q residuals plot, Retelling T2 plot,...

Principle Component Analysis (PCA) is an effective way not only to eliminate correlation among process variables and reduce the influence of noise and disturbance on system, but also to reserve enough information of original data characteristics needed for modeling a industrial complex process. Based on principle component model, detection and diagnosis analysis is carried out on a typical Heavy Oil Fractionator with multivariate statistical techniques such as Q residuals plot, Retelling T2 plot, principle scores plot and contributions plot. In addition, mean contributions curve is used instead of common contribution histogram to diagnose the source cause of faults.

对系统过程数据进行主元分析,建立主元模型,可以在保留原有数据信息特征的基础上消除变量关联和部分系统噪声干扰,从而简化系统分析的复杂度。建立正确的主元模型,结合多变量统计过程控制图(Q统计图,HotellingT2图,主元得分图,贡献图),是对过程对象的进行检测和诊断的一项发展中的技术。通过对一个典型的重油分馏塔运行过程的故障监测与诊断分析,进一步说明了主元模型在确定故障特征方向和多变量统计控制图在监测和诊断故障源上的作用和有效性。同时采用了平均贡献图来直观明确地判别引起系统故障的主要原因。

Monitoring batch processes to ensure their safe operation and to produce consistently high-quality products is needed. Multiway principal component analysis(MPCA) is a nonlinear modeling methodology for batch process. Previous publications have focused upon the application of statistical analysis for sensor fault identification through data reconstruction. These reconstruction based methods do not address the problem of fault propagation to other sensor measurements and as a consequence misleading fault identification...

Monitoring batch processes to ensure their safe operation and to produce consistently high-quality products is needed. Multiway principal component analysis(MPCA) is a nonlinear modeling methodology for batch process. Previous publications have focused upon the application of statistical analysis for sensor fault identification through data reconstruction. These reconstruction based methods do not address the problem of fault propagation to other sensor measurements and as a consequence misleading fault identification can result. Based on MPCA, this paper use a multiple sensor faults diagnosis method. By using the T~2-statistic in conjunction with the associated contribution plot, multiple sensor faults can be identified in a sequential manner. Simulations verify the effectiveness of the method.

 研究一种基于MPCA的多传感器故障诊断方法.这种方法把过程测量空间分为主元子空间和残差子空间.在残差子空间,首先用Q统计指标检测出传感器是否存在故障,如果Q统计指标超限,在主元子空间应用T2统计量和相应的T2统计量的贡献率,识别出引起过程异常的主要传感器变量并剔除.然后用同样的方法继续判断其它的传感器故障.仿真实例验证了该方法的有效性.

 
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