Tracking individual parts in manufacturing processes helps to lower production cost by assigning a full record of production steps to each part, and therefore enabling targeted quality control. A novel approach is presented, which shows that the printing quality can be monitored using images of the printed result only. Analysing data from long-term tests shows which characteristics of the part marking are suitable to be used as indicators for maintenance needs.

This paper discusses the deployment of an internet of things (IoT)-based model to monitor the fermentation of tea in a tea factory in Kenya. The model uses deep learning to detect the optimum fermentation of tea as fermentation progresses. To further improve on the results, a majority voting technique based on regions is used. The model shows promising results, and its predictions correlated well with those of the experts.

Detecting faults in bearings is indispensable for the maintenance of many industrial machines or components. The faults are detected by recognizing patterns in vibration data that are measured at the bearing housing. The method can be trained with data of variable revolution speeds, therefore reducing the effort for the acquisition of training data. Moreover, incipient faults can be detected before they cause severe damage to the equipment.

A test method for narrowband wireless sensor/actuator networks is presented, facilitating affordable and efficient performance and compliance tests. Employing frequency/time division multiple access (F/TDMA) techniques and protocol synchronization, the complexity can siginificantly be reduced. Thus, complete communication testing becomes technically feasible and affordable.