CENDRe: Concept Extraction with Natural Domain Representations

Neural networks used for time-series classification---like detecting mechanical faults from sensor data---are often black boxes, making it hard to trust their predictions in safety-critical settings. CENDRe improves interpretability by extracting \"concepts\" from a CNN\'s internal representations, automatically determining how many concepts exist and precisely localizing them in both time and frequency domains. Tested on bearing-fault data, it identifies exactly which frequency bands drive predictions, matching regions engineers already inspect. This has direct applications in industrial equipment monitoring, predictive maintenance, and any domain needing trustworthy explanations for CNN-based diagnostic models. Authors: Antonia Holzapfel, Andres Felipe Posada Moreno, Sebastian Paper: https://arxiv.org/abs/2607.29621v1