Root Cause Analysis in the Industrial Domain using Knowledge Graphs: A Case Study on Power Transformers

Jorge Martinez-Gil*, Georg Buchgeher, David Gabauer, Bernhard Freudenthaler, Dominik Filipiak, Anna Fensel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)


In the industrial domain, developing solutions that allow the identification, understanding, and correction of faults is essential due to the cost of handling such situations. However, to date, there are not many solutions capable of facilitating the human operator to discern the causes and possible solutions for a specific fault. In this work, we present knowledge graph-driven root cause analysis for working with faults in the industrial domain, based on three points of action: reasoning from the current state of machines or processes, failure classification using rules, and advanced querying using graph-query languages. We have conducted a power transformer case study that revealed that our proposed approach could be considered competitive as it has outperformed several alternative machine learning classifiers.

Original languageEnglish
Pages (from-to)944-953
Number of pages10
JournalProcedia Computer Science
Publication statusPublished - 8 Mar 2022
Event3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 - Linz, Austria
Duration: 19 Nov 202121 Nov 2021


  • Knowledge Graphs
  • Manufacturing
  • Production
  • Root Cause Analysis


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