[1]耿志强,王 尊,顾祥柏,等.基于总体平均经验模态分解残差的故障诊断方法[J].南京理工大学学报(自然科学版),2015,39(03):293.
 Geng Zhiqiang,Wang Zun,Gu Xiangbai,et al.Fault diagnosis method based on EEMD residual[J].Journal of Nanjing University of Science and Technology,2015,39(03):293.
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基于总体平均经验模态分解残差的故障诊断方法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
39卷
期数:
2015年03期
页码:
293
栏目:
出版日期:
2015-06-30

文章信息/Info

Title:
Fault diagnosis method based on EEMD residual
作者:
耿志强1王 尊1顾祥柏12林晓勇1
1.北京化工大学 信息科学与技术学院,北京 100029; 2.中国石化炼化工程(集团)股份有限公司,北京 100101
Author(s):
Geng Zhiqiang1Wang Zun1Gu Xiangbai12Lin Xiaoyong1
1.College of Information Science and Technology,Beijing University of Chemical Technology, Beijing 100029,China; 2.Sinopec Engineering(Group)Co.,Ltd.,Beijing 100101,China
关键词:
总体平均经验模态分解 残差 故障诊断 贝叶斯信息准则 希尔伯特谱 田纳西-伊士曼过程
Keywords:
ensemble empirical mode decomposition residuals fault diagnosis Bayesian information criterions Hilbert spectral Tennessee Eastman process
分类号:
TP277
摘要:
为了提高化工过程故障诊断的效率,基于残差对故障状态具有敏感性以及经验模态分解(EMD)无需建模仅依据输入输出数据分析的优势,提出了一种基于总体平均经验模态分解(EEMD)残差进行故障诊断的新方法。基于历史数据的6σ控制图,确定残差的故障诊断控制限。利用在线实时数据采用贝叶斯信息准则在线确定EEMD的移动窗口。基于移动窗口的采样数据,在线获得EEMD残差最大值的变化,结合相应的故障诊断控制限在线诊断故障并确定故障发生时间及原因。该文方法与传统的希尔伯特谱分析方法相比,具有可在线诊断故障的优势,提高了故障诊断的准确率。将该文方法用于田纳西-伊士曼(TE)过程的故障在线诊断,验证了其有效性。
Abstract:
To improve the efficiency of fault diagnosis for chemical processes,a new fault diagnosis method based on ensemble empirical mode decomposition(EEMD)residuals is provided considering the sensitivity of residuals to fault states and the advantages of empirical mode decomposition(EMD)that it need not model and bases on input and output data analysis only.The control limits of the residuals fault diagnosis are defined based on 6σ control charts.EEMD moving windows are determined online by using Bayesian information criterions(BIC)and online real-time data.The EEMD maxium residual changes are calculated based on moving window sampling data,and the time and causes of faults are diagnosed online by combing with corresponding control limits of residual fault diagnosis.Compared with traditional Hilbert spectral analysis methods,the proposed method realizes online fault diagnosis and improves diagnosis accuracy.The effectiveness of the proposed method is verified by Tennessee Eastman(TE)process simulation.

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备注/Memo

备注/Memo:
收稿日期:2014-09-29 修回日期:2015-03-25
基金项目:国家自然科学基金(61374166); 教育部博士点基金(20120010110010); 中央高校基本科研业务费(JD1413; YS1404)
作者简介:耿志强(1973-),男,博士,教授,博士生导师,主要研究方向:过程建模、优化与故障诊断,E-mail:gengzhiqiang@mail.buct.edu.cn。
引文格式:耿志强,王尊,顾祥柏,等.基于总体平均经验模态分解残差的故障诊断方法[J].南京理工大学学报,2015,39(3):293-300.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2015-06-30