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Anomaly detection for process monitoring based on machine learning technique

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Abstract

Anomaly detection is critical to process modeling, monitoring, and control since successful execution of these engineering tasks depends on access to validated data. The industrial process is uncertain in several situations, and the available information is formalized in terms of intervals. This article deals with the diagnostic of uncertain systems by multivariate static analysis. Linear Principal Component Analysis (PCA) and nonlinear Kernel PCA (KPCA) are generally used to deal with certain systems; they exploit single-valued variables. While in real situations these data are marred by uncertainties, these uncertainties cause difficulties in making decision in relation to the presence of defects. Thus, we have studied a recent and robust solution which consists in capturing the variability of multivariate observations by interval variables. In the first part, we treated a fault detection strategy based on interval PCA in the case of static linear systems. It includes first of all a comparative study between the deferent methods of detection of faults with interval PCA in which we proposed a new detection statistics of faults. In the second part, we studied a fault detection strategy based on interval KPCA method; we propose a reduction approach to solve the problem of nonlinearity and uncertainty and the problem of large data. The proposed fault detection methods are illustrated by synthetic data with an in-depth study and comparison using simulations of the air quality monitoring network and the Tennessee Eastman process.

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Abbreviations

VAR:

The variance

COV:

The covariance

Moy:

The average

M :

Number of system variables

\(N\) :

Number of samples

\(\ell\) :

Number of selected

R :

Number of reduced of samples

\(\left[ X \right]\) :

Interval data matrix

\(X^{{\text{m}}}\) :

Center matrix

\(X^{{\text{r}}}\) :

Range matrix

\(X_{{\text{R}}}\) :

The reduced data matrix

\({\text{LB}}\) :

Lower bound

\({\text{UB}}\) :

Upper bound

\(\overline{K}^{{\text{R}}}\) :

Reduced kernel matrix for UB

\(\underline{K}^{{\text{R}}}\) :

Reduced kernel matrix for LB

\(K_{{{\text{Rmr}}}}\) :

Reduced kernel matrix based on center and range

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Author information

Authors and Affiliations

  1. Electronics and Micro-Electronic Laboratory, Faculty of Sciences of Monastir, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia

    Imen Hamrouni

  2. National Engineering School of Monastir, University of Monastir, Monastir, Tunisia

    Hajer Lahdhiri & Okba Taouali

  3. Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia

    Khaoula Ben Abdellafou, Ahamed Aljuhani & Okba Taouali

Authors
  1. Imen Hamrouni
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  2. Hajer Lahdhiri
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  3. Khaoula Ben Abdellafou
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  4. Ahamed Aljuhani
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  5. Okba Taouali
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Correspondence to Okba Taouali.

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Appendix

Appendix

See Table 8

Table 8 Cost of KPCA, RRKPCA, IKPCA_UL, IKPCA_CR, RRIKPCA_CR, RRIKPCA_UL
Full size table

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Hamrouni, I., Lahdhiri, H., Ben Abdellafou, K. et al. Anomaly detection for process monitoring based on machine learning technique. Neural Comput & Applic 35, 4073–4097 (2023). https://doi.org/10.1007/s00521-022-07901-2

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