Fault detection in a batch process using a bayesian model

T. Nonaka, Yoshiyuki yamashita, Mutsumi Suzuki

Research output: Contribution to journalArticlepeer-review

Abstract

Application of Bayesian dynamic modeling to fault detection is developed for a nonstationary batch process. In the modeling, the observed time series are expressed in several specific components such as local polynomial trend, observation noise and globally stationary autoregressive component. To illustrate the method, detection of a fault in an operation of a stirred vessel with a heater is presented.From the sequential probability ratio test of the model estimation error, the fault can be detected successfully with high sensitivity.

Original languageEnglish
Pages (from-to)465-469
Number of pages5
JournalJOURNAL of CHEMICAL ENGINEERING of JAPAN
Volume26
Issue number5
DOIs
Publication statusPublished - 1993

Keywords

  • Batch Process
  • Bayesian Statistics
  • Fault Detection
  • Hypothesis Test
  • Process System
  • State Space Method
  • Time Series Analysis

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)

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