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Process Monitoring Using Multiscale Methods

  • Chris Aldrich
  • Lidia Auret
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Principal component analysis is widely used in disturbance detection, isolation and diagnosis in industrial and chemical processes, and several extensions of the basic principal component methodology have been considered in previous chapters to handle features such as autocorrelation in data, time–frequency localization and nonlinearity. In this chapter, a statistical process control approach based on singular spectrum analysis is proposed. The method involves expressing a time series as the sum of identifiable components whose basis functions are obtained from measurements. Using decomposition by means of singular spectrum analysis, a multimodal representation is obtained that can be used together with existing statistical process control methods to construct a novel process monitoring scheme. It is observed that singular spectrum analysis can perform significantly better than other methods, particularly in detecting mean shift changes. However, the performance of the approach can degrade in the presence of parameter changes, as well as excessive autocorrelation of the variables.

Keywords

Control Chart Exponentially Weighted Moving Average Statistical Process Control Multivariate Time Series Singular Spectrum Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Chris Aldrich
    • 1
    • 2
  • Lidia Auret
    • 2
  1. 1.Western Australian School of MinesCurtin UniversityPerthAustralia
  2. 2.Department of Process EngineeringUniversity of StellenboschStellenboschSouth Africa

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