From knowledge graphs to probabilistic models for system-level diagnostics

Résumé fourni par la source

The increasing complexity of high-tech systems poses a significant challenge on service organizations tasked with the timely identification of the root causes of unexpected downtimes.While datadriven methods are effective for diagnosing frequently occurring issues, or those affecting a large number of systems, rare issues suffer from data scarcity, necessitating alternative approaches.This paper presents a twostep model-based methodology that leverages system architecture information to support diagnosing of systems for which little data is available.Firstly, system design and observability information, including diagnostic tests, is captured in a knowledge graph.Secondly, the knowledge graph is queried and transformed into a probabilistic graphical model.This is fully automated using transformation rules based on the ontology underlying the knowledge graph.The probabilistic graphical model then infers the most likely causes of failure using measurement data, and guides service engineers by suggesting cost-effective diagnostic actions.This paper outlines the proposed methodology, demonstrates its application on a small example system, and reports early-stage validation findings from high-tech system cases.

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