Harmonizing Heterogeneous Diagnostic Data of a Vehicle Fleet for DataDriven Analytics Datadriven technologies, such as predictive maintenance, become increasingly important to todayu0027s automotive industry due to advancements of connected cars and OvertheAir technologies. A data source that has barely been used in the literature so far is diagnostic data, which is obtained by sending requests to the electronic control units of a vehicle. Diagnostic data can be collected costeffectively and is already available on a large scale to car manufacturers today. However, the use of diagnostic data is associated with some difficulties. The set of measured variables differs greatly between different vehicles of the same type due to different configurations and therefore differences in the electronic control units. In this contribution, we show how diagnostic data can be harmonized for the use of datadriven modeling. An heuristic threestep procedure is introduced to identify similar measured variables. Finally, our approach is verified on a synthetic data set. Future datadriven technologies are able to use larger and more costefficient data sets this way.

DataDriven Fault Diagnosis in EndofLine Testing of Complex Products Machine learning approaches may support various use cases in the manufacturing industry. However, these approaches often do not address the inherent characteristics of the real manufacturing data at hand. In fact, real data impose analytical challenges that have a strong influence on the performance and suitability of machine learning methods. This paper considers such a challenging use case in the area of EndofLine testing, i.e., the final functional check of complex products after the whole assembly line. Here, classification approaches may be used to support quality engineers in identifying faulty components of defective products. For this, we discuss relevant data sources and their characteristics, and we derive the resulting analytical challenges. We have identified a set of sophisticated datadriven methods that may be suitable to our use case at first glance, e.g., methods based on ensemble learning or sampling. The major contribution of this paper is a thorough comparative study of these methods to identify whether they are able to cope with the analytical challenges. This comprises the discussion of both fundamental theoretical aspects and major results of detailed experiments we have performed on the real data of our use case.