期刊文献+

工业过程数据异常检测的改进局部离群因子法 被引量:4

The outlier detection of industry process using improved local outlier factor
原文传递
导出
摘要 局部离群因子(LOF)是对过程数据的局部离群程度的定义,然而工业过程对数据异常检测的实时性要求高,要求出所有采样点的离群因子计算量较大。故本文对LOF算法进行相应的改进,采用k-近邻计算对象的局部可达密度,同时利用1种预处理采样点的方法CDC(Closest Distance to Center),通过计算每个点到中心点的距离先对采样点进行修剪,剔除大部分不可能是离群点的采样点,只需要计算剩余点改进的LOF值,从而提高离群点检测的效率。最终通过对TE过程数据仿真,说明在保证离群点检测准确性的情况下,相比于LOF缩短了算法运行的时间。 Local outlier factor is the defmition of the local outlier degree of process data. However, the examination of abnormal data of industrial process requires high real-time capability and a great deal calculation is required to calculate the local outlier factors of all sampling data. So the k-neighbours used to calculate the local density reachable are introduced to improve the LOF algorithm. Simultaneously, a preprocessing method called closest distance to center is mentioned in this paper. As a result, the sampling points are pruned by computing distances between every point and the center, and we can get rid of most points which are impossible to be the outliers. Then it is just needed to calculate the modified local outlier factors of the reminders, which improve the efficiency of detecting outliers. In the end, the simulation results of the dataset of TE process state the reduction the operation time compared with LOF algorithm with the guarantee of the veracity of outlier detection.
作者 何九虎 刘飞
出处 《计算机与应用化学》 CAS CSCD 北大核心 2013年第1期53-56,共4页 Computers and Applied Chemistry
基金 国家自然科学基金资助项目(61134007) 江苏高等学校优秀科技创新团队资助项目
关键词 局部离群因子 K-近邻 CDC local outlier factor, k-neighbours, closest distance to center
  • 相关文献

参考文献4

二级参考文献50

  • 1金澈清,钱卫宁,周傲英.流数据分析与管理综述[J].软件学报,2004,15(8):1172-1181. 被引量:161
  • 2熊家军,李庆华.信息熵理论与入侵检测聚类问题研究[J].小型微型计算机系统,2005,26(7):1163-1166. 被引量:14
  • 3倪巍伟,陆介平,陈耿,孙志挥.基于k均值分区的数据流离群点检测算法[J].计算机研究与发展,2006,43(9):1639-1643. 被引量:20
  • 4薛安荣,鞠时光,何伟华,陈伟鹤.局部离群点挖掘算法研究[J].计算机学报,2007,30(8):1455-1463. 被引量:96
  • 5Lu W,Han J W. Discovery of general knowledge in large spatial databases [C]// Proceedings of Far East Workshop on Geographic Information Systems. Singapore: World Scientific, 1993 : 275-289.
  • 6Hawkins D. Identification of Outliers [ M ]. London: UK: Chapman and Hall, 1980.
  • 7Shekhar S,Lu Changtien,Zhang Pusheng. A unified approach to detecting spatial outliers [J]. Geolnformatica, 2003,7 ( 2 ) : 139- 166.
  • 8Lu Changtien, Chen Dechang,Kou Yufeng. Algorithms for spatial outlier detection [ C ]//Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, Florida, USA: IEEE Computer Society,2003 : 597-600.
  • 9Lu Changtien, Chen Dechang, Kou Yufeng. Detecting spatial outliers with multiple attributes [C]//Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, Sacramento, California, USA : IEEE Computer Society,2003 : 122-128.
  • 10Hu Tianming, Sung S Y. A trimmed mean approach to finding spatial outliers [J]. Intelligent Data Analysis,2004 ( 8 ) : 79-95.

共引文献24

同被引文献36

  • 1Nomikos P and MacGregor J F. Monitoring batch processes using multi-way principal component analysis. AIChE J, 1994, 40: 1361-1375.
  • 2Nomikos P and MacGregor J F. Multivariate SPC charts for batch processes. Technometrics, 1995,37(1):41-59.
  • 3Nomikos P and MacGregor J F. Multi-way partial least squares in monitoring batch processes. Chemo-metrics and Intelligent Laboratory Systems, 1995,30:97-108.
  • 4Undey C and Cinar A. Statistical monitoring of multistage, multi-phase batch processes. IEEE Control Syst Mag, 2002, 22:40-52.
  • 5Camacho J, Pic J. Multi-phase principal component analysis for batch processes modeling. Chemometrics and Intelligent Laboratory Systems, 2006, 81(2):127-136.
  • 6Dong Weiwei, Yao Yuan, Gao Furlong. Phase analysis and identification method for multi-phase batch processes with partitioning Multi-way Principal Component Analysis (MPCA) model. Chinese Journal of Chemical Engineering, 2012, 20(6): 1l2I-1l27.
  • 7Camacho J, Pic J, Ferrer A. The best approaches in the on-line monitoring of batch processes based on PCA: dose the modeling structurem atter. Analytica Chimica Acta, 2009, 642(1-2):59-68.
  • 8罗俊伟.基于FCM的类合行聚类算法研究[D].重庆:重庆大学,2009.
  • 9田慧欣,毛志忠,赵珍.与软测量建模相结合的过失误差侦破新方法[J].仪器仪表学报,2008,29(12):2658-2662. 被引量:6
  • 10葛新权.回归模型应用的发展综述[J].北京信息科技大学学报(自然科学版),2009,24(3):1-6. 被引量:1

引证文献4

二级引证文献40

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