In industry, data-driven techniques have revolutionized manufacturing by collecting huge amounts of information during production and turning it into valuable information for process
optimization. While machine learning (ML) is a key technology and the main contributing factor for many recent success stories, we witness the transition of ML moving from the “virtual world” into “the wild”; this includes prominent applications in autonomous navigation, the Internet of Things, and Industry 4.0 applications. Evidently, this transition opens several real-world
challenges for ML that need to be addressed for closing the gap between both worlds.
We focus on an essential component in modern manufacturing systems – in data-driven machine condition monitoring. A crucial requirement for the widespread acceptance of ML-based
condition monitoring is to not only work accurately but to work reliably in every imaginable situation and to provide interpretations and uncertainty measurements of the model behavior.
In real-world situations, a manifold of disturbances and environmental influences can occur that need to be accounted for. Particular requirements for real-world system are: first, robustness in the presence of outliers, domain shifts, and corrupted data, second, learning and transferring knowledge from similar problems to counteract the limited availability of labeled data, and third, being aware of the model’s limits; finally, in safety-critical systems, it is equally important to achieve accurate predictions and to understand the behavior of a model.