An Improved Wavelet-Based Multivariable Fault Detection Scheme

Fouzi Harrou, Ying Sun, Muddu Madakyaru

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish (US)
Title of host publicationUncertainty Quantification and Model Calibration
PublisherIntechOpen
ISBN (Print)9789535132790
DOIs
StatePublished - Jul 5 2017

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