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

可解释性 机器学习 计算机科学 超参数 人工智能 特征选择 超声波传感器 过程(计算) 物理 声学 操作系统
作者
Alexander Bowler,Michael P. Pound,Nick J. Watson
出处
期刊:Ultrasonics [Elsevier BV]
卷期号:124: 106776-106776 被引量:18
标识
DOI:10.1016/j.ultras.2022.106776
摘要

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.
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