东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2015, Vol. 36 ›› Issue (6): 761-765.DOI: 10.12068/j.issn.1005-3026.2015.06.001

• 信息与控制 •    下一篇

稀疏性SVDD方法在故障检测中的应用研究

王国柱1， 刘建昌1， 李元2   

  1. (1. 东北大学 信息科学与工程学院， 辽宁 沈阳110819； 2. 沈阳化工大学 信息工程学院， 辽宁 沈阳110142)
  • 收稿日期:2014-04-22 修回日期:2014-04-22 出版日期:2015-06-15 发布日期:2015-06-11
  • 通讯作者: 王国柱
  • 作者简介:王国柱(1984-)，男，河南焦作人，东北大学博士研究生; 刘建昌(1960-)，男，辽宁沈阳人，东北大学教授，博士生导师; 李元(1964-)，女，辽宁沈阳人，沈阳化工大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61374137，61174119，61034006，60774070); 流程工业综合自动化国家重点实验室基础科研业务费资助项目(2013ZCX02-03).

An Applied Research of Sparsity SVDD Method to the Fault Detection

WANG Guo-zhu1， LIU Jian-chang1， LI Yuan2   

  1. 1. School of Information Science & Engineering， Northeastern University， Shenyang 110819，China; 2. Information Engineering School， Shenyang University of Chemical Technology， Shenyang 110142，China.
  • Received:2014-04-22 Revised:2014-04-22 Online:2015-06-15 Published:2015-06-11
  • Contact: WANG Guo-zhu
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摘要： 在支持向量数据描述(SVDD)方法的基础上，通过研究原始正常数据分布在高维映射空间内的稀疏特性，选取前k个高维分布边缘的数据点进行SVDD建模，用于解决SVDD方法处理大样本数据的缺陷，以及建模与过程监视时间长的问题.经过理论推导和仿真分析，验证了稀疏性SVDD建模方法可以有效地提高建模以及过程检测速度；对于大样本数据可以利用筛选后的小样本进行建模，解决了SVDD方法不能很好地处理大样本数据分类的问题；同时，此方法不影响故障检测的精度.在TE过程中的应用验证了该方法的有效性.

关键词: 稀疏性, SVDD, 稀疏性SVDD, 故障检测

Abstract: Fault detection based on the basic SVDD (support vector data description) method is not good at the processing of large sample data， and the modeling and process monitoring is time-consuming. The sparse characteristics of the original data in high dimension space was studied， according to which the first k high dimensional distribution edge data points were selected to carry out the SVDD modeling. Through theoretical derivation and simulation analysis， it was showed that the modeling and detection speed could be effectively improved by the proposed method， and the large sample data could be modeled by using the selected small sample， which could handle the classification problems of SVDD method on solving large sample data; meanwhile， this method did not affect the accuracy of fault detection. The effectiveness of the proposed method was illustrated by applying it to the monitoring of TE process.

Key words: sparsity, SVDD, sparsity SVDD, fault detection

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