Online reduced gaussian process regression based generalized likelihood ratio test for fault detection

R. Fezai, M. Mansouri, K. Abodayeh, H. Nounou, M. Nounou

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this paper we consider a new fault detection approach that merges the benefits of Gaussian process regression (GPR) with a generalized likelihood ratio test (GLRT). The GPR is one of the most well-known machine learning techniques. It is simpler and generally more robust than other methods. To deal with both high computational costs for large data sets and time-varying dynamics of industrial processes, we consider a reduced and online version of the GPR method. The online reduced GPR (ORGPR) aims to select a reduced set of kernel functions to build the GPR model and apply it for online fault detection based on GLRT chart. Compared with the conventional GPR technique, the proposed ORGPR method has the advantages of improving the computational efficiency by decreasing the dimension of the kernel matrix. The developed ORGPR-based GLRT (ORGPR-based GLRT) could improve the fault detection efficiency since it is able to track the time-varying characteristics of the processes. The fault detection performance of the developed ORGPR-based GLRT method is evaluated using a Tennessee Eastman process. The simulation results show that the proposed method outperforms the conventional GPR-based GLRT technique.

Original languageEnglish
Pages (from-to)30-40
Number of pages11
JournalJournal of Process Control
Volume85
DOIs
Publication statusPublished - Jan 2020

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Keywords

  • Fault detection (FD)
  • Gaussian process regression (GPR)
  • Generalized likelihood ratio test (GLRT)
  • Machine learning (ML)
  • Online reduced GPR
  • Tennessee eastman (TE) process

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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