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Dynamic data rectification using particle filters

Lookup NU author(s): Dr Tao Chen, Emeritus Professor Julian Morris, Professor Elaine Martin

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Abstract

The basis of dynamic data rectification is a dynamic process model. The successful application of the model requires the fulfilling of a number of objectives that are as wide-ranging as the estimation of the process states, process signal denoising and outlier detection and removal. Current approaches to dynamic data rectification include the conjunction of the Extended Kalman Filter (EKF) and the expectation-maximization algorithm. However, this approach is limited due to the EKF being less applicable where the state and measurement functions are highly non-linear or where the posterior distribution of the states is non-Gaussian. This paper proposes an alternative approach whereby particle filters, based on the sequential Monte Carlo method, are utilized for dynamic data rectification. By formulating the rectification problem within a probabilistic framework, the particle filters generate Monte Carlo samples from the posterior distribution of the system states, and thus provide the basis for rectifying the process measurements. Furthermore, the proposed technique is capable of detecting changes in process operation and thus complements the task of process fault diagnosis. The appropriateness of particle filters for dynamic data rectification is demonstrated through their application to an illustrative non-linear dynamic system, and a benchmark pH neutralization process. (C) 2007 Elsevier Ltd. All rights reserved.

Publication metadata

Author(s): Chen T, Morris J, Martin E

Publication type: Article

Publication status: Published

Journal: Computers & Chemical Engineering

Year: 2008

Volume: 32

Issue: 3

Pages: 451-462

ISSN (print): 0098-1354

ISSN (electronic): 1873-4375

Publisher: Pergamon

URL: http://dx.doi.org/10.1016/j.compchemeng.2007.03.012

DOI: 10.1016/j.compchemeng.2007.03.012

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