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Confidence limits for contribution plots in multivariate statistical process control using bootstrap estimates.

Hamid Babamoradi1, Frans van den Berg1, Åsmund Rinnan1

  • 1University of Copenhagen, Faculty of Science, Department of Food Science, Spectroscopy & Chemometrics Section, Rolighedsvej 30, DK-1958 Frederiksberg, Denmark.

Analytica Chimica Acta
|January 31, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces bootstrap re-sampling to establish confidence limits for contribution plots in Principal Component Analysis-based Multivariate Statistical Process Control (PCA-based MSPC). This method improves fault detection accuracy by comparing current process runs against historical data, reducing false alarms.

Keywords:
BootstrapConfidence limitFault diagnosisMultivariate statistical process controlProcess monitoringVariable contributions

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Area of Science:

  • Industrial Engineering
  • Statistical Process Control
  • Data Science

Background:

  • Contribution plots are vital in Multivariate Statistical Process Control (MSPC) for fault diagnosis.
  • Traditional interpretation of contribution plots can lead to false alarms due to natural process variations.
  • Existing methods lack reliable confidence limits for contribution plots, hindering accurate fault identification.

Purpose of the Study:

  • To develop a robust method for estimating confidence limits (CLs) for contribution plots in online PCA-based MSPC.
  • To enhance the accuracy of fault detection by comparing new process runs with historical normal operating conditions.
  • To address the limitations of asymptotic methods in providing CLs for contribution plots.

Main Methods:

  • Implementation of bootstrap re-sampling to generate confidence limits for contribution plots.
  • Application of Principal Component Analysis (PCA) for online MSPC.
  • Validation using an industrial batch process dataset.

Main Results:

  • The proposed bootstrap re-sampling method effectively generates confidence limits for contribution plots.
  • Comparison with previously reported CLs demonstrates the efficacy of the new strategy.
  • The method provides a more reliable basis for judging new production runs against historical data.

Conclusions:

  • Bootstrap re-sampling offers a viable solution for estimating confidence limits in PCA-based MSPC contribution plots.
  • This approach mitigates false readings caused by natural variations and measurement uncertainties.
  • The developed strategy enhances the reliability of fault detection in industrial processes.