1 Citation (Scopus)

Abstract

This paper introduces a new structure kernel principal component analysis (KPCA) that can successfully model symbolic interval-valued data for fault detection. In the proposed structure, interval KPCA (IKPCA) method is proposed to deal with interval-valued data. Two IKPCA models are proposed. The first model is based on the centers and ranges of intervals IKPCA CR and the second model is based the upper and lower bounds of intervals IKPCA UL . Residuals are generated and fault detection indices are computed. The aim of using IKPCA is to ensure robustness to false alarm without affecting the fault detection performance. The proposed fault detection approach is carried out using simulation example and Tennessee Eastman Process (TEP). The obtained results demonstrate the effectiveness of the proposed technique.

Original languageEnglish
Pages (from-to)36-45
Number of pages10
JournalChemical Engineering Science
DOIs
Publication statusPublished - 21 Sep 2019

Fingerprint

Nonlinear Process
Fault Detection
Fault detection
Data-driven
Interval
Principal component analysis
Kernel Principal Component Analysis
False Alarm
Model
Upper and Lower Bounds
Robustness

Keywords

  • Fault detection
  • Interval-valued data
  • Kernel PCA
  • Tennessee Eastman process

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

Cite this

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title = "Fault detection of uncertain nonlinear process using interval-valued data-driven approach",
abstract = "This paper introduces a new structure kernel principal component analysis (KPCA) that can successfully model symbolic interval-valued data for fault detection. In the proposed structure, interval KPCA (IKPCA) method is proposed to deal with interval-valued data. Two IKPCA models are proposed. The first model is based on the centers and ranges of intervals IKPCA CR and the second model is based the upper and lower bounds of intervals IKPCA UL . Residuals are generated and fault detection indices are computed. The aim of using IKPCA is to ensure robustness to false alarm without affecting the fault detection performance. The proposed fault detection approach is carried out using simulation example and Tennessee Eastman Process (TEP). The obtained results demonstrate the effectiveness of the proposed technique.",
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author = "Mohamed-Faouzi Harkat and Majdi Mansouri and Mohamed Nounou and Hazem Nounou",
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N2 - This paper introduces a new structure kernel principal component analysis (KPCA) that can successfully model symbolic interval-valued data for fault detection. In the proposed structure, interval KPCA (IKPCA) method is proposed to deal with interval-valued data. Two IKPCA models are proposed. The first model is based on the centers and ranges of intervals IKPCA CR and the second model is based the upper and lower bounds of intervals IKPCA UL . Residuals are generated and fault detection indices are computed. The aim of using IKPCA is to ensure robustness to false alarm without affecting the fault detection performance. The proposed fault detection approach is carried out using simulation example and Tennessee Eastman Process (TEP). The obtained results demonstrate the effectiveness of the proposed technique.

AB - This paper introduces a new structure kernel principal component analysis (KPCA) that can successfully model symbolic interval-valued data for fault detection. In the proposed structure, interval KPCA (IKPCA) method is proposed to deal with interval-valued data. Two IKPCA models are proposed. The first model is based on the centers and ranges of intervals IKPCA CR and the second model is based the upper and lower bounds of intervals IKPCA UL . Residuals are generated and fault detection indices are computed. The aim of using IKPCA is to ensure robustness to false alarm without affecting the fault detection performance. The proposed fault detection approach is carried out using simulation example and Tennessee Eastman Process (TEP). The obtained results demonstrate the effectiveness of the proposed technique.

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KW - Kernel PCA

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