Multiple model based LPV soft sensor development with irregular/missing process output measurement

doi:10.1016/j.conengprac.2011.10.007

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Multiple model based LPV soft sensor development with irregular/missing process output measurement

2012-02-01

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DOI：10.1016/j.conengprac.2011.10.007

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Abstract

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Data-driven soft sensors have been applied extensively in process industry for process monitoring and control. Linear soft sensors, which are only valid within a relatively small operating envelope, are considered to be insufficient in practice when the processes transit among several operating modes. Moreover, owing to a variety of causes such as malfunction of sensors, multiple rate sampling scheme for different process variables, etc., missing data problem is commonly experienced in process industry. In this paper, soft sensor development with irregular/missing output data is considered and a multiple model based linear parameter varying (LPV) modeling scheme is proposed for handling nonlinearity. The efficiency of the proposed algorithm is demonstrated through several numerical simulation examples as well as a pilot-scale experiment. It is shown through the comparison with the traditional missing data treatment methods in terms of the parameter estimation accuracy that the developed soft sensors enjoy improved performance by employing the expectation-maximization (EM) algorithm in handling the missing process data and model switching problem. (C) 2011 Elsevier Ltd. All rights reserved.

Keywords:

Bayesian methods

Soft sensors

Expectation-maximization method

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