  Concept-based explanation methods, such as Concept Activation Vectors, are
potent means to quantify how abstract or high-level characteristics of input
data influence the predictions of complex deep neural networks. However,
applying them to industrial prediction problems is challenging as it is not
immediately clear how to define and access appropriate concepts for individual
use cases and specific data types. In this work, we investigate how to leverage
established concept-based explanation techniques in the context of bearing
fault detection with deep neural networks trained on vibration signals. Since
bearings are prevalent in almost every rotating equipment, ensuring the
reliability of intransparent fault detection models is crucial to prevent
costly repairs and downtimes of industrial machinery. Our evaluations
demonstrate that explaining opaque models in terms of vibration concepts
enables human-comprehensible and intuitive insights about their inner workings,
but the underlying assumptions need to be carefully validated first.
