Research on a knowledge modelling methodology for fault diagnosis of machine tools based on formal semantics. Issue 2 (April 2017)

• Record Type: Journal Article
• Authors: Zhou, Qiang; Yan, Ping; Xin, Yang
• Is Part Of: Advanced engineering informatics. Volume 32:Issue 2(2017:Apr.)
• Page Start: 92
• Page End: 112
• Publication Date: 2017-04
• Subjects: Machine tool -- Fault diagnosis -- Knowledge modelling -- Ontology -- Formal semantics; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2017.01.002
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Fault diagnosis is a critical activity in PHM (Prognostics and Health Management) of machine tools due to its great significance in such efforts as prolonging lifespan, improving production efficiency, and reducing production costs. An efficient knowledge model is necessary to build an intelligent fault diagnosis system. There have been several achievements in knowledge representation and modelling. However, due to their various purposes and depths, the established knowledge models are less compatible, reusable or transplantable, which restricts knowledge sharing and integration. A knowledge modelling methodology for fault diagnosis of machine tools based on formal semantics (KMM-MTFD) is proposed in this paper to build an open, shared, and scalable ontology-based knowledge model of fault diagnosis of various machine tools (OKM-MTFD). First, the proposed predicate-logic-based analysis method of fault elements is adopted to study the fault diagnosis domain and extract the common domain knowledge, which enables the establishment of the core ontology of OKM-MTFD to assure formal semantics. Next, using the proposed two-stage classification method of fault elements and external ontology reference methods, the core ontology can be extended into OKM-MTFD for a type or a specific machine tool. The knowledge reasoning and querying methods based on OWL axioms, SWRL rules, special fault attributes and SPARQL are provided to utilize the knowledge base efficiently. Finally, an ontology-based knowledge model and knowledge base of a hobbing machine tool is presented to exemplify the validity of the proposed KMM-MTFD.