期刊文献+

多元指数加权移动平均主元分析的微小故障检测 预览 被引量:16

Incipient fault detection of multivariate exponentially weighted moving average principal component analysis
在线阅读 下载PDF
收藏 分享 导出
摘要 主元分析(principal component analysis,PCA)是一种有效的数据分析方法,在故障诊断与状态监测方面已得到广泛应用.多元指数加权移动平均-主元分析(multivariate exponentially weighted moving average principal component analysis,MEWMA-PCA)方法用于解决PCA不能有效检出微小故障的问题.本文深入研究了MEWMA-PCA中EWMA影响主元分析进行故障检测的机制,导出了MEWMA-PCA可检出微小故障的原因.本文确定了MEWMA-PCA中遗忘因子λ、单传感器故障幅值和迟延时间三者的关系,并进行了数值仿真和火电厂磨煤机组运行状态的仿真实验.实验结果验证了MEWMA-PCA中EWMA提高PCA的监测性能的机制,并给出了根据系统实际要求来选取合适的遗忘因子值,从而在规定的时间内检出微小故障的实例. The principal component analysis (PCA) is a useful tool for data analysis and has been widely used in fault de- tection and process monitoring. MEWMA-PCA (multivariate exponentially weighted moving average principal component analysis) is used to solve the problem where PCA cannot detect incipient faults properly. This paper further investigates the mechanism of the effect of EWMA on the fault detection of PCA in MEWMA-PCA. The reason that MEWMA-PCA can detect incipient faults is analyzed. The relationship among the forgetting factor, the detectable amplitude of a single sensor and the delay time introduced by EWMA is derived. Both numerical simulation results and historical data simulation result of a coal grinding unit in a power plant validate the mechanism of the improvement of fault detection by MEWMA-PCA. An example is given for showing the detection of an incipient fault within a specified time range satisfying the practice requirement, by setting appropriate forgetting factor.
作者 邱天 白晓静 郑茜予 朱祥 QIU Tian, BAI Xiao-jing, ZHENG Xi-yu, ZHU Xiang (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2014年第1期19-26,共8页 Control Theory & Applications
基金 国家重点基础研究发展计划"973"计划资助项目(2012CB215203) 国家自然科学基金重点资助项目(51036002) 中央高校基本科研业务费专项资金资助项目.
关键词 微小故障 主元分析 指数加权移动平均 故障检测 incipient faults principal component analysis (PCA) exponentially weighted moving average (EWMA) fault detection
作者简介 邱天(1976-),男,副教授,硕士生导师,主要研究方向为工业过程监测、故障检测、故障诊断,E-mail:qiutian@ncepu.edu.cn. 白晓静(1987-),女,硕士研究生,主要研究方向为故障诊断,E-mail:xiaoj_bai@163.com. 郑茜予(1989-),女,硕士研究生,主要研究方向为故障诊断,E-mail:xiyuer.zheng@gmail.com. 朱祥(1988-),男,硕士研究生,主要研究方向为过程监测,E-mail:xuchunhonghappy03@163.com.
  • 相关文献

参考文献13

  • 1李娟,周东华,司小胜,陈茂银,徐春红.微小故障诊断方法综述[J].控制理论与应用,2012,29(12):1517-1529. 被引量:55
  • 2WANG H, ZHOU H, HANG B. Number selection of principal com- ponents with optimized process monitoring performance [C] //Pro- ceedings of Decision and Control. Hangzhou, China: Zhejiang Uni- versity, 2004:4726 - 4731. 被引量:1
  • 3王海清,宋执环,李平.主元分析方法的故障可检测性研究[J].仪器仪表学报,2002,23(3):232-235. 被引量:10
  • 4AKHILESH J, RAJEEV U, SUMANA C. Exponentially weighted moving average scaled PCA for on-line monitoring of Tennessee Eastman challenge process [J]. International Journal of Systems, Al- gorithms & Applications, 2012, 2(12): 183 - 186. 被引量:1
  • 5ZHANG G, LI N, LI S. A modified multivariate EWMA control chart for monitoring process small shifts [C] //Proceedings of Modelling, Identification and Control. Shanghai: Shanghai JiaoTong University, 2011: 75-80. 被引量:1
  • 6DUNIA R, QIN S J, EDGAR T F, et al. Identification of faulty sen- sors using principal component analysis [J]. AIChE Journal, 2004, 42(10): 2797 - 2812. 被引量:1
  • 7QIN S J, YUE H, DUNIA R. Self-validating inferential sensors with application to air emission monitoring [J]. Industrial & Engineering Chemistry Research, 1997, 36(5): 1675 - 1685. 被引量:1
  • 8QIN S J, YUE H, DUNIA R. A self-validating inferential sensor for emission monitoring [C] //Proceedings of American Control. Austin, TX, USA: Texas University, 1997:473 - 477. 被引量:1
  • 9CHEN J, LIAO C M, LIN F R J, et al. Principle component analysis based control charts with memory effect for process monitoring [J]. Industrial & Engineering Chemistry Research, 2001, 40(6): 1516 - 1527. 被引量:1
  • 10葛志强,杨春节,宋执环.基于MEWMA-PCA的微小故障检测方法研究及其应用[J].信息与控制,2007,36(5):650-656. 被引量:10

二级参考文献93

共引文献66

同被引文献129

引证文献16

二级引证文献21

投稿分析

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部 意见反馈