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An Improved Wavelet-Based Multivariable Fault Detection Scheme

In: Uncertainty Quantification and Model Calibration

Author

Listed:
  • Fouzi Harrou
  • Ying Sun
  • Muddu Madakyaru

Abstract

Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)-based Q and EWMA methods and MSPLS-based Q method.

Suggested Citation

  • Fouzi Harrou & Ying Sun & Muddu Madakyaru, 2017. "An Improved Wavelet-Based Multivariable Fault Detection Scheme," Chapters, in: Jan Peter Hessling (ed.), Uncertainty Quantification and Model Calibration, IntechOpen.
    • Handle: RePEc:ito:pchaps:113431
    DOI: 10.5772/intechopen.68947
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    More about this item

    Keywords

    data uncertainty; multiscale representation; fault detection; data-driven approaches; statistical monitoring schemes;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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