State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

Marion McAfee, Mandana Kariminejad, Albert Weinert, Saif Huq, Johannes D. Stigter and David Tormey
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Marion McAfee: Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Institute of Technology Sligo, Ash Lane, F91 YW50 Sligo, Ireland
Mandana Kariminejad: Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Institute of Technology Sligo, Ash Lane, F91 YW50 Sligo, Ireland
Albert Weinert: Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Institute of Technology Sligo, Ash Lane, F91 YW50 Sligo, Ireland
Saif Huq: School of Computing and Digital Media, London Metropolitan University, 166-220 Holloway Rd., London N7 8DB, UK
Johannes D. Stigter: Biometris, Department of Mathematical and Statistical Methods, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
David Tormey: Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Institute of Technology Sligo, Ash Lane, F91 YW50 Sligo, Ireland

Sustainability, 2022, vol. 14, issue 6, 1-33

Abstract: State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables that 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 across sectors including industrial robotics, material synthesis and processing, semiconductor, and additive manufacturing. It is shown that state estimation algorithms can play a key role in manufacturing systems for accurately monitoring and controlling 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 controlling distributed manufacturing systems.

Keywords: state observer; Kalman filter; particle filter; sustainable manufacturing; soft sensor; digital twin (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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