A Neuro-Symbolic Approach for Anomaly Detection and Complex Fault Diagnosis Exemplified in the Automotive Domain
Tim Bohne, Anne-Kathrin Patricia Windler, Martin Atzmueller · 2023
This paper presents an iterative, hybrid neuro-symbolic approach for anomaly detection and complex fault diagnosis, enabling knowledge-based (symbolic) methods to complement (neural) machine learning methods and vice versa. We demonstrate an instantiation of this novel diagnosis system with applicability in a practically relevant real-world context, specifically the automotive domain. Explainability is indispensable for diagnosis and arises naturally in the system through the specific interplay of neural and symbolic methods. The presented architecture can be considered as a blueprint which is generally transferable to various diagnostic problems and domains.