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Statistical process monitoring of time-dependent data

Summary: During the last decades, we evolved from measuring few process variables at sparse intervals to a situation in which a multitude of variables are measured at high speed. This evidently provides opportunities for extracting more information from processes and to pinpoint out-of-control situations, but transforming the large data streams into valuable information is still a challenging task. In this contribution we will focus on the analysis of time-dependent processes since this is the scenario most often encountered in practice, due to high sampling systems and the natural behavior of many real-life applications. The modeling and monitoring challenges that statistical process monitoring (SPM)techniques face in this situation will be described and possible routes will be provided. Simulation results as well as a real-life data set will be used throughout the article.

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  • Topics: Engineering
  • Keywords: Time series, Data, Monitoring, Principal components, Autocorrelation, First order autoregressive models
  • Author: De Ketelaere, Bart; Rato, Tiago; Schmitt, Eric; Hubert, Mia
  • Journal: Quality Engineering