State estimators in soft sensing and sensor fusion for sustainable manufacturing

McAfee, Marion, Kariminejad, Mandana, Weinert, Albert, Huq, Saif, Stigter, Johannes D. and Tormey, David (2022) State estimators in soft sensing and sensor fusion for sustainable manufacturing. Sustainability, 14 (6). pp. 1-34. ISSN 2071-1050


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Abstract / Description

State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables which cannot be measured directly (’soft sensing’) or for which only noisy, intermittent, delayed, indirect or unreliable measurements are available, perhaps from multiple sources (’sensor fusion’). In this paper we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes. It is shown that state estimation algorithms can play a key role in manufacturing systems to accurately monitor and control processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and control of distributed manufacturing systems.

Item Type: Article
Uncontrolled Keywords: state observer; Kalman filter; particle filter; sustainable manufacturing; soft sensor; 13 digital twin
Subjects: 500 Natural Sciences and Mathematics > 510 Mathematics
600 Technology > 670 Manufacturing
Department: School of Computing and Digital Media
Depositing User: Saif Huq
Date Deposited: 11 Apr 2022 08:25
Last Modified: 11 Apr 2022 08:25


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