Multiclass data classification using fault detection-based techniques

Nour Basha, M. Ziyan Sheriff, Costas Kravaris, Hazem Nounou, Mohamed Nounou

Research output: Contribution to journalArticle

Abstract

Multiclass classification of big data is a subject of broad interest in machine learning research nowadays, where it is necessary to extract important features from a dataset's variables in order to accurately detect unique variations between different classes of data. In this paper, we will discuss the application of a novel combination of different fault detection-based techniques towards the problem of multiclass classification of different types of faults found in the benchmark Tennessee Eastman Process. Moreover, the fault detection performance and data classification accuracy of our proposed method is compared to the respective performances of multiple data-driven methods tabulated in literature, including deep neural networks. The results show that a combined application of multiple fault detection techniques, in tandem with the one-versus-all and all-versus-all binary decomposition methods, can provide a competitive multiclass classification accuracy, comparative to other more complex methods in literature.

Original languageEnglish
Article number106786
JournalComputers and Chemical Engineering
Volume136
DOIs
Publication statusPublished - 8 May 2020

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Keywords

  • Binary decomposition
  • Fault detection
  • Generalized likelihood ratio test
  • Hypothesis testing
  • Interval aggregation
  • Moving-window
  • Multiclass classification
  • Principal component analysis

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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