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 R² 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.

第四次工业革命旨在使用物联网，云计算和机器学习等工业数字技术集成整个制造过程，以提高过程生产率，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，因此是此转换中的关键技术。超声波传感器具有低成本，在线，无创且能够在不透明系统中运行的优势。监督的机器学习模型可以将超声传感器数据与有关制造材料和过程的有用信息相关联。但是，这需要对过程材料进行参考测量，以标记每个数据点以进行模型训练。带标签的数据通常很难在工厂环境中获得，因此，需要一种没有这种方法的模型训练方法。这项工作比较了两种域适应方法，以跨流程传输模型，因此不需要标记数据即可准确地监视目标流程。比较的两种方法是单一特征转移学习方法和使用三个特征的转移成分分析。超声波波形对于所使用的传感器，连接步骤和接触压力是唯一的。因此，仅研究了少量的可转移特征。案例研究使用了两个与工业相关的过程：混合和清洁管道中的污垢。使用了一种反射模式超声传感技术，该技术可监测从容器壁和工艺材料之间的界面反射的声波。全面的，单一特征方法产生的预测精度最高：分别对混合和清洁完成程度进行分类时分别高达96.0％和98.4％；和R^2个值分别高达0.947和0.999，以预测直到完成的剩余时间。这些结果突出了将超声测量与转移学习技术结合起来以监视工业过程的潜力。虽然，还需要进一步的工作来研究各种影响，例如更改源域和目标域之间的传感器位置。