Ontology-Based Method for Fault Diagnosis of Loaders

Sensors
Feixiang XuBingwei Cao

Abstract

This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

References

May 15, 2015·IEEE Transactions on Neural Networks and Learning Systems·Peng ShiRamesh K Agarwal

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Citations

Mar 26, 2019··Bin LiuZheyuan Ding

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Methods Mentioned

BETA
SELECT

Software Mentioned

Jena
Sparql
METHONTOLOGY
Protégé
Windows
Visual Studio2017
Sparql ( RDF Query Language
TOVE
SWRL
SENSUS

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