HSFDONES: A Self-Leaning Ontology-Based Fault Diagnosis Expert System Framework - Computer and Computing Technologies in Agriculture IV - Part IV
Conference Papers Year : 2011

HSFDONES: A Self-Leaning Ontology-Based Fault Diagnosis Expert System Framework

Xiangbin Xu
  • Function : Author
  • PersonId : 1013087

Abstract

HSFDONES is an expert system fault diagnosis which makes the fault diagnosis working more intelligently, HSFDONES uses the ontology-based self-leaning theory and technology to build fault diagnosis expert system. The fault diagnosis knowledge structure is defined and the relevant structure ontology and core fault ontology is researched in HSFDONES; the fault diagnosis data warehouse is built, the decision tree and Apriori algorithm are used to acquire fault knowledge to realize HSFDONES’s ontology self-learning. HSFDONES offers system framework for building intelligent fault diagnosis system. Finally the agricultural machinery’s hydraulic fault diagnosis expert system was developed on the basis of the framework.
Fichier principal
Vignette du fichier
978-3-642-18369-0_54_Chapter.pdf (261.48 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01564878 , version 1 (19-07-2017)

Licence

Identifiers

Cite

Xiangbin Xu. HSFDONES: A Self-Leaning Ontology-Based Fault Diagnosis Expert System Framework. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.460-466, ⟨10.1007/978-3-642-18369-0_54⟩. ⟨hal-01564878⟩
51 View
104 Download

Altmetric

Share

More