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Modeling and monitoring of nonlinear multi-mode processes based on similarity measure-KPCA

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Abstract

A new modeling and monitoring approach for multi-mode processes is proposed. The method of similarity measure(SM) and kernel principal component analysis (KPCA) are integrated to construct SM-KPCA monitoring scheme, where SM method serves as the separation of common subspace and specific subspace. Compared with the traditional methods, the main contributions of this work are: 1) SM consisted of two measures of distance and angle to accommodate process characters. The different monitoring effect involves putting on the different weight, which would simplify the monitoring model structure and enhance its reliability and robustness. 2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace. Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    **ao-gang Wang  (王小刚), Li-wei Huang  (黄立伟) & Ying-wei Zhang  (张颖伟)

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  1. **ao-gang Wang  (王小刚)
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  2. Li-wei Huang  (黄立伟)
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  3. Ying-wei Zhang  (张颖伟)
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Corresponding author

Correspondence to **ao-gang Wang  (王小刚).

Additional information

Foundation item: Projects(61273163, 61325015, 61304121) supported by the National Natural Science Foundation of China

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Wang, Xg., Huang, Lw. & Zhang, Yw. Modeling and monitoring of nonlinear multi-mode processes based on similarity measure-KPCA. J. Cent. South Univ. 24, 665–674 (2017). https://doi.org/10.1007/s11771-017-3467-z

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  • DOI: https://doi.org/10.1007/s11771-017-3467-z

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