Fault diagnosis of industrial processes based on weighted k-nearest neighbor reconstruction analysis

被引：0
|
作者：
Wang, Guo-Zhu [1 ]
Liu, Jian-Chang [1 ]
Li, Yuan [2 ]
Shang, Liang-Liang [1 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning
[2] College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, 110142, Liaoning
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2015年 / 32卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Data reconstruction; Fault detection; Fault diagnosis; K-NN; Maximize reduce index (MRI);
D O I：
10.7641/CTA.2015.50028
中图分类号：
学科分类号：
摘要：
The k-nearest-neighbor (k-NN) is an effective fault detection method based on data driven, which has been widely used in industrial process monitoring. However, investigating the root causes of abnormal events is a crucial task when the process faults have been detected, and isolating the faulty variables provides additional information for investigating the root causes of the faults. In this paper, a novel fault diagnosis method is derived using weighted k-NN reconstruction on maximize reduce index (MRI) variables, it can effectively identify the faulty variables and locate the faulty sensors. A numerical simulation is provided to validate the performance of weighted k-NN in the aspect of data reconstruction; it also show that this method is suitable not only for a single sensor fault, but also with good results for multiple sensor faults which are existing simultaneously or in propagation through variable correlation. Finally, this method is applied to TE chemical process successfully. ©, 2015, South China University of Technology. All right reserved.
引用
收藏
页码：873 / 880
页数：7
相关论文
共 30 条
  • [1] Zhou D., Li G., Li Y., Fault Detection and Diagnosis Industrial Process Based on Data-Driven, (2011)
  • [2] Zhou D., Hu Y., Fault diagnosis techniques for dynamic systems, Acta Automatica Sinica, 35, 6, pp. 748-758, (2009)
  • [3] Li J., Zhou D., Si X., Et al., Review of incipient fault diagnosis methods, Control Theory & Applications, 29, 12, pp. 1515-1529, (2012)
  • [4] Nomikos P., Macgregor J.F., Monitoring batch processes using multiway principal component analysis, American Institute of Chemical Engineers Journal, 40, 8, pp. 1361-1375, (1994)
  • [5] Kresta J., Macgregor J.F., Marlin T.E., Multivariate statistical monitoring of process operating performance, Canadian Journal of Chemical Engineering, 69, 1, pp. 35-47, (1991)
  • [6] Wise B.M., Ricker N.L., Recent advances in multivariate statistical process control: improving robustness and sensitivity, Proceedings of the IFAC Intemational Symposium, ADCHEM'91, pp. 125-130, (1991)
  • [7] Macgregor J.F., Jaeckle C., Kiparissides C., Et al., Process monitoring and diagnosis by multiblock PLS methods, AIChE Journal, 40, 5, pp. 826-838, (1994)
  • [8] Nomikos P., Macgregor J.F., Monitoring batch process using multiway principal component analysis, AIChE Journal, 40, 8, pp. 1361-1375, (1994)
  • [9] Qiu T., Bai X., Zheng X., Et al., Incipient fault detection of multivariate exponentially weighted moving average principal component analysis, Control Theory & Applications, 31, 1, pp. 19-26, (2014)
  • [10] Zhang J., Cao J., Gao F., Et al., Fault diagnosis of complex system based on nonlinear spectrum and kernel principal component analysis, Control Theory & Applications, 29, 12, pp. 1558-1564, (2012)
123