SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (11): 1782-1792.DOI: 10.1016/j.cjche.2015.09.004

• 过程系统工程与过程安全 • 上一篇    下一篇

A local and global statistics pattern analysis method and its application to process fault identification

Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, Lianfang Cai   

  1. College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China
  • 收稿日期:2015-02-08 修回日期:2015-07-26 出版日期:2015-11-28 发布日期:2015-12-18
  • 通讯作者: Xuemin Tian
  • 基金资助:

    Supported by the National Natural Science Foundation of China (61273160, 61403418), the Natural Science Foundation of Shandong Province (ZR2014FL016), and the Fundamental Research Funds for the Central Universities (14CX06132A).

A local and global statistics pattern analysis method and its application to process fault identification

Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, Lianfang Cai   

  1. College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China
  • Received:2015-02-08 Revised:2015-07-26 Online:2015-11-28 Published:2015-12-18
  • Contact: Xuemin Tian
  • Supported by:

    Supported by the National Natural Science Foundation of China (61273160, 61403418), the Natural Science Foundation of Shandong Province (ZR2014FL016), and the Fundamental Research Funds for the Central Universities (14CX06132A).

摘要: Traditional principal component analysis (PCA) is a second-ordermethod and lacks the ability to provide higherorder representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incorporated into PCAmodel tomake full use of various statistics of data variables effectively. However, thesemethods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality preserving projections within the PCA, is proposed to utilize various statistics and preserve both local and global information in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simulation results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.

关键词: Principal component analysis, Local structure analysis, Statistics pattern analysis, Reconstruction-based contribution, Fault diagnosis

Abstract: Traditional principal component analysis (PCA) is a second-ordermethod and lacks the ability to provide higherorder representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incorporated into PCAmodel tomake full use of various statistics of data variables effectively. However, thesemethods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality preserving projections within the PCA, is proposed to utilize various statistics and preserve both local and global information in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simulation results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.

Key words: Principal component analysis, Local structure analysis, Statistics pattern analysis, Reconstruction-based contribution, Fault diagnosis