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A review of ultrasonic sensing and machine learning methods to monitor industrial processes

Bowler, Alexander L.; Pound, Michael P.; Watson, Nicholas J.



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.


Bowler, A. L., Pound, M. P., & Watson, N. J. (2022). A review of ultrasonic sensing and machine learning methods to monitor industrial processes. Ultrasonics, 124, Article 106776.

Journal Article Type Article
Acceptance Date May 26, 2022
Online Publication Date May 28, 2022
Publication Date Aug 1, 2022
Deposit Date Jun 12, 2022
Publicly Available Date Jun 14, 2022
Journal Ultrasonics
Print ISSN 0041-624X
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 124
Article Number 106776
Keywords Acoustics and Ultrasonics
Public URL
Publisher URL


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