Studies and Application of Heavy Equipment Fault Diagnosis System

Yi Gan; Sha Liu; Wen Bo Zhu

doi:10.4028/www.scientific.net/AMR.225-226.399

Paper Titles

GM(1,1) Model of Moving Average Operation to the Original Sequence and its Application
p.381

A System Design and Implementation of Home Monitoring Based on ZigBee Technology
p.385

The Numerical Simulation of the Aquifer Thermal Energy Storage Technology
p.390

Suggestion of Introducing the Human Reliability into Dam Risk Analysis
p.395

Studies and Application of Heavy Equipment Fault Diagnosis System
p.399

Research on Moving Human Positioning in Complex Background Video
p.403

Application of Unascertained Measure-Entropy Evaluation Model for Program Selection of Shaft Construction under Complex Conditions
p.407

The Integration of Wireless Location Technology and GIS in the Logistics Information System and its Application
p.411

A Novel Algorithm Based on the Degree Tree for Graph Isomorphism
p.417

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 225-226Studies and Application of Heavy Equipment Fault...

Studies and Application of Heavy Equipment Fault Diagnosis System

Abstract:

Analyze the ways to get fault information for heavy equipment fault diagnosis system, which are the control system of the device, layout sensors to get the key performance parameters, and human-computer interaction. In order to improve accuracy and efficiency of the diagnostic system, the methods of fault location tree retrieval and similar case retrieval are applied respectively according to the difference of fault information content in the diagnosis information database. The diagnosis system introduced in the paper gets effective initial application in the heavy equipment fault diagnosis system.

You might also be interested in these eBooks

Advanced Research on Automation, Communication, Architectonics and Materials

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 225-226)

Pages:

399-402

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.225-226.399

Citation:

Cite this paper

Online since:

April 2011

Authors:

Yi Gan, Sha Liu, Wen Bo Zhu

Keywords:

Fault Diagnosis System, Fault Retrieval, Heavy Equipment

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] DU Haifeng, WANG Na, ZHANG Jinhua, et al. Fault diagnosis strategy based on complex network analysis. Journal of Mechanical Engineering, Vol. 46(2010), pp.90-96.

Google Scholar

[2] Yang Changhao. Intelligent Method Research for Fault Diagnosis of Mechanism Based On Uncertainty Theory. University of Science and Technology of China. A dissertation for doector's degree. (2009).

Google Scholar

[3] Chiu C, Chiu N H, Hsu C I. Intelligent aircraft maintenance support system using genetic algorithms and case-based reasoning. Int J Adv Manuf Technol, Vol. 24(2004), pp.440-446.

DOI: 10.1007/s00170-003-1707-x

Google Scholar

[4] LEI Yaguo, HE Zhengjia, ZI Yanyang. Fault Diagnosis Based on Novel Hybrid Intelligent Model. Chinese Journal of Mechanical Engineering. Vol. 44(2008), pp.112-117.

DOI: 10.3901/jme.2008.07.112

Google Scholar

[5] ZHENG Xiao-xia, QIAN Feng. Faulty Diagnosis based on Rough Sets Decision Tree Model and Ant Colony Algorith Sets. System Engineering-theory & Practice. Vol. 3(2007), pp.140-144.

Google Scholar

[6] JIANG Zhan-si, CHEN Li-ping, LUO Nian-meng. Similarity analysis in nearest-neighbor case retrieval. Computer Integrated Manufacturing Systems. 2Vol. 13(2007), pp.1165-1168.

Google Scholar