An Hybrid Expert Fault Diagnosis System for CNC System

You Dong Chen; Jin Jun Ye; Hua Song Min; Mei Hua Han

doi:10.4028/www.scientific.net/AMR.189-193.1562

Paper Titles

A Novel Ultrasonic Motor Driver Based on Two Phase PWM Signals
p.1543

Prediction of Processing Parameters of Laser Clad TiC/NiCrBSiC Coatings on Ti6Al4V by Finite Element Analysis
p.1547

3D FEM Modeling of Ball End Milling of HSM H13 Die Steel with Cutter Inclination
p.1551

The Deformation Analysis for Flank Milling of Thin-Walled Rectangle Plate Based on Flexible Force Model
p.1555

An Hybrid Expert Fault Diagnosis System for CNC System
p.1562

The Parametric Design of the External Transmission of Involute Beveloid Gear with Parallel Axes
p.1567

Virtual Measurement of Machining Errors Based on Comparison Method of Three Models
p.1571

Head Surface Reconstruction and Correction of Reverse Engineering Based on CATIA's
p.1575

Finite Cylindrical Shell Element Method which Analyses Local Stability of Buried Stiffened-Penstock
p.1580

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 189-193An Hybrid Expert Fault Diagnosis System for CNC...

An Hybrid Expert Fault Diagnosis System for CNC System

Abstract:

The CNC system is a complex mechatronics system, which make it difficult to diagnose fault. Expert system for fault diagnosis that utilizes domain knowledge and the profiles of experts to fix the problem of the complex system has become an important issue. A hybrid expert fault system combining the rule-base reasoning (RBR) with case-based reasoning (CBR) for CNC system is proposed. The combination can trouble-shoot rapidly, improve the CNC system reliability and maintainability. The hybrid system is implemented by using QT and SQLITE database. The experiment result of the system shows that the system diagnosis efficiently and accurately.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 189-193)

Pages:

1562-1566

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.189-193.1562

Citation:

Cite this paper

Online since:

February 2011

Authors:

You Dong Chen, Jin Jun Ye, Hua Song Min, Mei Hua Han

Keywords:

CBR, CNC System, Expert System, Fault Diagnosis, RBR

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Aixiang Chen, Lihua Wu, Yunfei Jiang. Research on intelligent diagnosis system with machine learning[J]. Computer Engineering and Application, 2008, 44(22) , 31-34(in Chinese).

Google Scholar

[2] hui Shi, wei Wang, yi Gao. General fault diagnosis expert system development platform[J]. Computer Measurement& Control, 2005, 13(11) , 1167-1168(in Chinese).

Google Scholar

[3] Luka J, Stubhan F. Mobile diagnosis[J]. In: IEEE International Vehicle Electronic Conference, Changchun, China, 1999, 215-220.

Google Scholar

[4] Achmad Widodo, Bo-Suk Yang, Tian Han. Combination of independent component analysis and support vector machines for intelligent faults[J]. Expert System with Application, In Press, Corrected Proof, Available online 4, Jan, 2006, 236-238.

DOI: 10.1016/j.eswa.2005.11.031

Google Scholar

[5] Hatzilygeroudis, I. and Prentzas, J. Integrating (rule, neural networks) and cases for knowledge representation and reasoning in expert systems, Expert Systems with Applications, (2004), 27(1), 63-64.

DOI: 10.1016/j.eswa.2003.12.004

Google Scholar