Data-Driven Fault Diagnosis in End-of-Line 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 End-of-Line 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 data-driven 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.