State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing
نویسندگان
چکیده
State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in dynamical system given the sensor measurements related states. They can be used to derive fast accurate estimates that cannot measured directly (‘soft sensing’) or which only noisy, intermittent, delayed, indirect, unreliable available, perhaps from multiple sources (‘sensor fusion’). In this paper, we introduce concepts main methods state estimation review recent applications improving sustainability manufacturing processes across sectors industrial robotics, material synthesis processing, semiconductor, additive manufacturing. It is shown play key role systems accurately monitoring controlling improve efficiencies, lower environmental impact, enhance product quality, feasibility processing more sustainable raw materials, ensure safer working environments humans. We discuss current emerging trends using as framework combining physical knowledge with other data distributed systems.
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ژورنال
عنوان ژورنال: Sustainability
سال: 2022
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su14063635