A fault detection approach using relative principal component analysis

OUYANG GaoQiang;CAO LiuLin

Journal of Beijing University of Chemical Technology ›› 2014, Vol. 41 ›› Issue (4) : 112-116.

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Journal of Beijing University of Chemical Technology ›› 2014, Vol. 41 ›› Issue (4) : 112-116.
机电工程和信息科学

A fault detection approach using relative principal component analysis

  • OUYANG GaoQiang;CAO LiuLin
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Abstract

The eigenvalues of the covariance matrix are almost the same in principal component analysis (PCA), and the single index T2 or squared prediction error (SPE) has often been utilized when using relative principal component analysis (RPCA) in fault detection. However, some important information in the fault-detection process is omitted when using this single index, and incorrect detection results can be obtained. To solve this problem, a comprehensive index has been introduced, by combining the index T2 and SPE in an effective way. Then, the combined index can be used in fault detection by relative principal component analysis in a typical Tennessee Eastman (TE) process. By comparing the simulation results in terms of the single index SPE and the combined index, it can be seen that the warning time was earlier and number of false alarms was less when using the combined index in the process of fault detection. The outcomes of this study demonstrate the effectiveness and feasibility of the method proposed.

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OUYANG GaoQiang;CAO LiuLin. A fault detection approach using relative principal component analysis[J]. Journal of Beijing University of Chemical Technology, 2014, 41(4): 112-116

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