Dynamic transfer soft sensor for concept drift adaptation?

doi:10.1016/j.jprocont.2023.01.012

Dynamic transfer soft sensor for concept drift adaptation?

2023-03-01

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DOI：10.1016/j.jprocont.2023.01.012

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Abstract

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Data-driven soft sensor technology has been widely used in process monitoring, quality prediction, etc. However, there are dynamic time-varying and concept drift problems in industrial processes, making the accurate modeling of the soft sensor is still a challenging task. To solve the above problems, this paper proposes a concept drift adaptive dynamic partial least squares method. The method maps highdimensional process data into a low-dimensional latent variable subspace. Dynamic information is introduced by establishing the dynamic regression relationship between process latent variables and quality latent variables. At the same time, transfer learning is used to align second-order statistics between latent variables of data from different distributions to solve the concept drift problem. The experiments on multiple industrial datasets show that the method proposed in this paper can effectively reduce prediction errors and improve the generalization ability of the model. (c) 2023 Elsevier Ltd. All rights reserved.

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Soft sensor

Concept drift

Transfer learning

Dynamic partial least squares

Dynamic transfer soft sensor for concept drift adaptation?

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