A tutorial on inferential control and its applications

Joseph, Babu

doi:10.1109/acc.1999.782334

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…In this sense, technologies and strategies have been developed to use the available information of other variables in the process and computational models infer the value of those that cannot be directly measured [1][2][3][4][5]. These strategies have been used in several contexts, such as oil refinery [6], chemical industry [7], air conditioning systems [8], automobiles [9], semiconductor manufacturing [10], and the quantification of properties determined by human perception (such as smell, taste, color, etc), among others [11][12][13][14][15][16][17]. Depending on the context, the type of indirect measurement strategy addressed in this paper can be referred to as soft sensor, virtual sensor, estimator, inferential sensor, virtual metrology, soft metrology, among others.…”

Section: Introductionmentioning

confidence: 99%

Soft metrology based on machine learning: a review

Vallejo

Espriella

Gómez-Santamaría

et al. 2019

Meas. Sci. Technol.

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Soft metrology has been defined as a set of measurement techniques and models that allow the objective quantification of properties usually determined by human perception such as smell, sound or taste. The development of a soft metrology system requires the measurement of physical parameters and the construction of a model to correlate them with the variables that need to be quantified. This paper presents a review of indirect measurement with the aim of understanding the state of development in this area, as well as the current challenges and opportunities; and proposes to gather all the different designations under the term soft metrology, broadening its definition. For this purpose, the literature on indirect measurement techniques and systems has been reviewed, encompassing recent as well as a few older key documents to present a time line of development and map out application contexts and designations. As machine learning techniques have been extensively used in indirect measurement strategies, this review highlights them, and also makes an effort to describe the state of the art regarding the determination of uncertainty. This study does not delve into developments and applications for human and social sciences, although the proposed definition considers the use that this term has had in these areas.

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