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

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

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.

Citation

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. https://doi.org/10.1016/j.ultras.2022.106776

• 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
• Electronic ISSN: 1874-9968
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 124
• Article Number: 106776
• DOI: https://doi.org/10.1016/j.ultras.2022.106776
• Keywords: Acoustics and Ultrasonics
• Public URL: https://nottingham-repository.worktribe.com/output/8492799
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0041624X2200083X?via%3Dihub