Generating knowledge in maintenance from Experience Feedback

• Published in: Knowledge-based systems Vol. 68; pp. 4 - 20
• Main Authors: Potes Ruiz, Paula, Kamsu Foguem, Bernard, Grabot, Bernard
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-09-2014; Elsevier
• Subjects: Association rules mining; Chemical and Process Engineering; Computerized Maintenance Management System; Conceptual Graphs; Engineering Sciences; Experience Feedback; Knowledge formalization; Maintenance; Conceptual Graphs; Experience Feedback; Maintenance; Computerized Maintenance Management System; Knowledge formalization; Association rules mining; Knowledge Formalization; Association Rules Mining
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2014.02.002

•The databases related to maintenance activities are a potential source of knowledge.•An ontology is used for modeling the information present in the experiences stored in the database.•A rule mining algorithm extracts association rules from past experiences.•Conceptual Graphs allow analyse the extracted knowledge.
Knowledge is nowadays considered as a significant source of performance improvement, but may be difficult to identify, structure, analyse and reuse properly. A possible source of knowledge is in the data and information stored in various modules of industrial information systems, like CMMS (Computerized Maintenance Management Systems) for maintenance. In that context, the main objective of this paper is to propose a framework allowing to manage and generate knowledge from information on past experiences, in order to improve the decisions related to the maintenance activity. In that purpose, we suggest an original Experience Feedback process dedicated to maintenance, allowing to capitalize on past activities by (i) formalizing the domain knowledge and experiences using a visual knowledge representation formalism with logical foundation (Conceptual Graphs); (ii) extracting new knowledge thanks to association rules mining algorithms, using an innovative interactive approach; and (iii) interpreting and evaluating this new knowledge thanks to the reasoning operations of Conceptual Graphs. The suggested method is illustrated on a case study based on real data dealing with the maintenance of overhead cranes.