1. Automatic Detection of Railway Track Defects at Goldschmidt Group (Artur Suchwalko and Marcin Kowalski)

Ensuring railway track safety is crucial. Currently, the track (rail, sleeper, fastener) defect detection relies foremost on human visual inspection. Achieving full automation of the track inspection (its defect detection) is important for ensuring track safety and to reduce maintenance cost. For this purpose, GRAW (Goldschmidt Group) with the help of QuantUp developed a custom software product. Rail track images used for analysis were acquired from a linear camera. Computer Vision, Machine Learning and optimisation methods were applied to detect sleepers being cracked, broken, or covered by ballast. This session start off by presenting the solution. Next, it will describe the challenges met along the way and how they were managed in business and analytical dimension

2. Predictive Maintenance: The Curse of Little Failure Data (Marcus Groß)

A major focus of Industry 4.0 is the optimization of manufacturing by analyzing the impact of production parameters on quality. The central conflict of this domain is avoiding errors first of all while deriving insights about failure causations from the very limited amount of failure data. Framed with a case study, we show how the application of suitable AI algorithms can solve this special type of “explore-or-exploit” dilemma and identify complex failure patterns in the production process.

3. Predictive Maintenance in Action: Leakage Localization (Arvin Arora / Nils Funke)

Air leaks lead to high energy losses in pneumatic systems. The result is unplanned downtime & high costs. Early detection & precise location of leaks can reduce maintenance costs & unplanned downtime. The challenges of a method suitable for practical use here are: few signals, heterogeneity, complexity, ltd. training data, nonlinear behavior, localization. We will discuss how you can successfully meet these challenges, using the example of an already implemented use case with Emerson (Aventics).