Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (4): 242-251.doi: 10.23940/ijpe.23.04.p3.242251

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An ILP Approach to Learn MKNF+ Rules for Fault Diagnosis

Samiya Bouarroudja,b,* and Zizette Boufaidaa   

  1. aLIRE Laboratory, University of Constantine 2 - Abdelhamid Mehri, Ali Mendjeli Campus, Constantine, Algeria;
    bDepartment of Computer Science, Université of 20 août 1955 of Skikda, Skikda, Algeria
  • Contact: *E-mail address:

Abstract: Building rules on ontology is the main task of mining the logical layer of the Semantic Web. A major effort has been made to develop algorithms capable of efficiently processing relational data and complex background knowledge. One of the promising technologies used in this effort is inductive logic programming (ILP). Steam boilers are an important equipment in power plants, and boiler trips can lead to a complete shutdown of the plant. Thus, it is essential to detect possible boiler trips in critical times to maintain normal and safe operating conditions. To address this challenge, the automatic rule-learning approach is used in this study to diagnose rule extraction. The learning examples are event sequences obtained by simulating an industrial steam boiler model. The ontology is considered as prior conceptual knowledge in ILP to induce supervision rules. The latter are eventually introduced into a scenario recognition system capable of continuously analyzing the event flow arriving at the supervisory center and alerting the operator when a fault situation is detected.

Key words: industry4, ontology, semantic web of things, hybrid language MKNF+, inductive logic programming, knowledge discovery