Process data de-noising using wavelet transform

Article type: Research Article

Authors: Bakhtazad, A.^a | Palazoglu, A.^b | Romagnoli, J.A.^{a; *}

Affiliations: [a] Orica Laboratory of Process Systems Engineering, Department of Chemical Engineering, University of Sydney, New South Wales 2006, Sydney, Australia | [b] Department of Chemical Engineering and Materials Science, University of California, Davis, CA 95616, USA

Correspondence: [*] Corresponding author. Tel.: +61-2-93514797; fax: +61-2-93512854. E-mail address: jose@chem.eng.usyd.edu.au (J.A. Romagnoli)

Abstract: The recovery of process information from noisy data (de-noising) is studied by investigating the classical solution of the estimation problem first. Next, the effectiveness of wavelet-based algorithms for data recovery is considered. A novel method based on coefficient de-noising according to WienerShrink method of wavelet thresholding is proposed. Simulation results are presented, highlighting the advantages of the de-noising method over the classical approaches based on the mean square error criterion.

Keywords: Wavelet transform, Noise reduction, Estimation theory

DOI: 10.3233/IDA-1999-3403

Journal: Intelligent Data Analysis, vol. 3, no. 4, pp. 267-285, 1999