Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom.
Ultrasonics. 2021 Aug;115:106468. doi: 10.1016/j.ultras.2021.106468. Epub 2021 May 18.
The fourth industrial revolution is set to integrate entire manufacturing processes using industrial digital technologies such as the Internet of Things, Cloud Computing, and machine learning to improve process productivity, efficiency, and sustainability. Sensors collect the real-time data required to optimise manufacturing processes and are therefore a key technology in this transformation. Ultrasonic sensors have benefits of being low-cost, in-line, non-invasive, and able to operate in opaque systems. Supervised machine learning models can correlate ultrasonic sensor data to useful information about the manufacturing materials and processes. However, this requires a reference measurement of the process material to label each data point for model training. Labelled data is often difficult to obtain in factory environments, and so a method of training models without this is desirable. This work compares two domain adaptation methods to transfer models across processes, so that no labelled data is required to accurately monitor a target process. The two method compared are a Single Feature transfer learning approach and Transfer Component Analysis using three features. Ultrasonic waveforms are unique to the sensor used, attachment procedure, and contact pressure. Therefore, only a small number of transferable features are investigated. Two industrially relevant processes were used as case studies: mixing and cleaning of fouling in pipes. A reflection-mode ultrasonic sensing technique was used, which monitors the sound wave reflected from the interface between the vessel wall and process material. Overall, the Single Feature method produced the highest prediction accuracies: up to 96.0% and 98.4% to classify the completion of mixing and cleaning, respectively; and R values of up to 0.947 and 0.999 to predict the time remaining until completion. These results highlight the potential of combining ultrasonic measurements with transfer learning techniques to monitor industrial processes. Although, further work is required to study various effects such as changing sensor location between source and target domains.
第四次工业革命将使用物联网、云计算和机器学习等工业数字技术整合整个制造过程，以提高工艺生产力、效率和可持续性。传感器收集优化制造过程所需的实时数据，因此是这一转型的关键技术。超声波传感器具有成本低、在线、非侵入式和能够在不透明系统中运行的优点。监督机器学习模型可以将超声波传感器数据与有关制造材料和工艺的有用信息相关联。然而，这需要对工艺材料进行参考测量，以便为模型训练标记每个数据点。在工厂环境中，通常很难获得标记的数据，因此需要一种无需此数据即可训练模型的方法。这项工作比较了两种域自适应方法来跨过程转移模型，以便在不要求精确监测目标过程的情况下不需要标记数据。比较的两种方法是单特征迁移学习方法和使用三个特征的迁移成分分析。超声波波形对使用的传感器、附件过程和接触压力是独特的。因此，只研究了少数可转移的特征。使用了两个工业相关的过程作为案例研究：混合和管道结垢的清洗。使用反射模式超声波传感技术，该技术监测从容器壁和工艺材料之间的界面反射的声波。总体而言，单特征方法产生了最高的预测精度：分别达到 96.0%和 98.4%来分类混合和清洗的完成；以及高达 0.947 和 0.999 的 R 值来预测完成之前剩余的时间。这些结果强调了将超声波测量与迁移学习技术相结合来监测工业过程的潜力。然而，需要进一步研究各种影响，例如在源域和目标域之间改变传感器位置。
