Analysis of the applicability of fault detection and failure prediction based on unsupervised learning and Monte Carlo simulations for real devices in the industrial automobile production Machine learning and statisticalbased methods have been widely used to estimate the Remaining Useful Life of devices. However, in most of the cases, the transferability of these methods to production environments is complicated. The majority of these algorithms have been tested by using simulated data, or by the construction of testbeds. The complication is generated by some real conditions in such environments which are not considered during the algorithm development. This research addresses those drawbacks by considering real data delivered by a production device in order to construct the right condition monitoring parameter. This parameter will be obtained by analyzing the correlation among available data, and failure on the critical element on the machine. Moreover, the benefits and drawbacks of several datadriven approaches are analyzed, and a method is proposed based on the combination of the best properties of each one.

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.