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SCIENCE CHINA Information Sciences, Volume 64 , Issue 5 : 159204(2021) https://doi.org/10.1007/s11432-018-9807-7

Fault diagnosis of industrial process based on the optimal parametric t-distributed stochastic neighbor embedding

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  • ReceivedNov 18, 2018
  • AcceptedJan 31, 2019
  • PublishedJun 15, 2020

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61573050, 61473025), Fundamental Research Funds for the Central Universities of China (Grant No. XK1802-4), and Open-Project Grant Funded by the State Key Laboratory of Synthetical Automation for Process Industry at the Northeastern University (Grant No. PAL-N201702).


Supplement

Appendixes A and B.


References

[1] Zhang A, Peng K, Shardt Y A W. A Comparison of Different Statistics for Detecting Multiplicative Faults in Multivariate Statistics-Based Fault Detection Approaches. IEEE access, 2018, 96: 541-553. Google Scholar

[2] Zhu J, Ge Z, Song Z. Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data. Annu Rev Control, 2018, 46: 107-133 CrossRef Google Scholar

[3] He Q P, Qin S J, Wang J. A new fault diagnosis method using fault directions in Fisher discriminant analysis. AIChE J, 2005, 51: 555-571 CrossRef Google Scholar

[4] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290: 2323-2326 CrossRef PubMed ADS Google Scholar

[5] Maaten L V D. Learning a parametric embedding by preserving local structure. J Mach Learn Res, 2009, 5: 384-391. Google Scholar

[6] Hinton G E, Salakhutdinov R R. Reducing the Dimensionality of Data with Neural Networks. Science, 2006, 313: 504-507 CrossRef PubMed ADS Google Scholar

[7] Kouropteva O, Okun O, Pietik$\ddot{a}$inen M. Selection of the optimal parameter value for the locally linear embedding algorithm. In: Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery, Singapore, 2002. 359--363. Google Scholar

[8] Wang R, Wang J, Zhou J. Fault diagnosis based on the integration of exponential discriminant analysis and local linear embedding. Can J Chem Eng, 2018, 96: 463-483 CrossRef Google Scholar

  • Figure 1

    (Color online) (a) Industrial process fault classification diagnosis flow chart based on parametric t-SNE;protect łinebreak (b) structure of fault classification model based on parametric t-SNE.