Combining informed data-driven anomaly detection with knowledge graphs for root cause analysis in predictive maintenance

Patrick Klein, Lukas Malburg, Ralph Bergmann

Abstract: Highlights•Applying three AI techniques (SPARQL - a Query Language for Resource Description Framework (RDF), Case-Based Reasoning (CBR), and Symbolic-Driven Neural Reasoning (SDNR)) for knowledge-based Root Cause Analysis (RCA) by leveraging typical explanations (i.e.,causative data streams) provided by data-driven anomaly detection models.•Proposing an informed deep self-supervised one-class anomaly detection approach that integrates domain knowledge in the form of time series relationships derived from the knowledge graph and knowledge graph embeddings.•Presentation of a general Failure Mode, Effects & Analysis (FMEA) ontology to model expert knowledge about faults and failures that is also instantiated for the used data.