Bowler, A.L. orcid.org/0000-0003-3209-2774, Pound, M.P. and Watson, N.J. orcid.org/0000-0001-5216-4873 (2022) A review of ultrasonic sensing and machine learning methods to monitor industrial processes. Ultrasonics, 124. 106776. ISSN 0041-624X
Abstract
Supervised machine learning techniques are increasingly being combined with ultrasonic sensor measurements owing to their strong performance. These techniques also offer advantages over calibration procedures of more complex fitting, improved generalisation, reduced development time, ability for continuous retraining, and the correlation of sensor data to important process information. However, their implementation requires expertise to extract and select appropriate features from the sensor measurements as model inputs, select the type of machine learning algorithm to use, and find a suitable set of model hyperparameters. The aim of this article is to facilitate implementation of machine learning techniques in combination with ultrasonic measurements for in-line and on-line monitoring of industrial processes and other similar applications. The article first reviews the use of ultrasonic sensors for monitoring processes, before reviewing the combination of ultrasonic measurements and machine learning. We include literature from other sectors such as structural health monitoring. This review covers feature extraction, feature selection, algorithm choice, hyperparameter selection, data augmentation, domain adaptation, semi-supervised learning and machine learning interpretability. Finally, recommendations for applying machine learning to the reviewed processes are made.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Ultrasonic measurements; Machine learning; Deep learning; Industrial digital technologies; Transfer learning; Domain adaptation |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds) The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Colloids and Food Processing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Jul 2024 09:44 |
Last Modified: | 12 Jul 2024 09:44 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ultras.2022.106776 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214616 |