首页|CPCA监视与知识诊断相结合的智能诊断

CPCA监视与知识诊断相结合的智能诊断

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
将多元统计分析技术与基于知识的故障诊断方法相结合,提出了一种过程智能监视与故障诊断方法.利用多元统计一致主元分析(CPCA)方法对过程异常工况进行监视,以找到故障发生区域及发生时段;针对故障发生区域.将CPCA方法得到的整体及局部的统计指标的定量信息、统计指标超限与否的定性信息以及得分及贡献率等综合信息提供给故障区域的基于知识的专家系统.从连退过程的应用可看出,该方法可检测出单变量监视难以检测的故障,并得到可靠的故障诊断结论.
Intelligent fault diagnosis by combination of CPCA monitoring and knowledge-based diagnosis
Combining the techniques of multivariate statistical process monitoring and knowledge-based fault diagnosis,an intelligent monitoring and fault diagnosis method for industrial process is proposed in this paper.The multivariate statistical consensus principal component analysis (CPCA) method is adopted to monitor the process,and to determine where and when the fault occurs.Moreover,the whole and regional statistical indices quantitative information as well as qualitative information,and the scores and contribution plot information are obtained by the CPCA method,and provided to the regional knowledge-based expert-system.Thus,from the application of this method to the continuous annealing process,the proper monitoring results which could not be achieved by the univariate moni toring and the reliable diagnosis conclusions can be achieved.

fault diagnosismultivariate statistical process monitoringknowledge-based diagnosisindustry processCPCA

刘强、秦泗钊、张颖伟、柴天佑

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东北大学流程工业综合自动化重点实验室,辽宁,沈阳,110004

南加州大学化工系,洛杉矶,90089

东北大学自动化研究中心,辽宁,沈阳,110004

故障诊断 多元统计过程监视 基于知识的诊断 工业过程 一致主元分析

国家重点基础研究发展规划(973计划)高等学校学科创新引智计划教育部科学技术研究重大项目国家高技术研究发展计划(863计划)

2009CB320600B080153080072006AA040307

2009

华中科技大学学报(自然科学版)
华中科技大学

华中科技大学学报(自然科学版)

CSTPCDCSCD北大核心EI
影响因子：0.813
ISSN：1671-4512
年,卷(期)：2009.37(z1)
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