Abstract

Autocorrelated data arise in a variety of processes. To statistically monitor such processes, special statistical tools are needed to account for these correlations. The most common set of tools for such purpose is the autoregressive integrated moving average (ARIMA) models. Implementation of ARIMA models requires a fair amount of background understanding of how these models work, because the model selection step is essential. In this paper, we propose a new monitoring technique based on the use of hidden Markov models. The proposed monitoring method is powerful yet simple to use technique because it only requires a basic knowledge in statistics. Simulation results show that the proposedmethod performs similar to the ARIMA models in terms of average run length for detecting out of control processes and false alarms.

Original languageEnglish (US)
Pages (from-to)1379-1387
Number of pages9
JournalQuality and Reliability Engineering International
Volume30
Issue number8
DOIs
StatePublished - Dec 1 2014

Keywords

  • ARIMA models
  • Hidden Markov models
  • Process monitoring

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Process monitoring using hidden markov models'. Together they form a unique fingerprint.

  • Cite this