Neural Network Based Expert System for Steel Bar Pipeline Fault Diagnosis

Abstract:

In modern continuing steel bars production pipeline, there are various equipments and automatic controls combining with mechanism, electricity, hydraulic pressure and aerodynamic. This field of fault detection and diagnosis deals with design of computer-based automated system that can assist plant operators. The neural network based expert system have advantages of parallel distributed processing, high robust, fault tolerance, adaptive and self-organization. Applying neural network based expert system for the condition detection and fault diagnosis of steel bars pipeline can reduce the economic loss caused by system downtime.

• Periodical: Advanced Materials Research (Volume 421)
• Pages: 590-594
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.421.590
• Online since: December 2011
• Authors: Jian Yin, Li Ting Mei
• Keywords: Expert System, Fault Diagnosis, Neural Network (NN), Steel Bars Pipeline

References

[1] Vaidyanathan: Process Fault Detection and Diagnosis using Neural Network, Purdue University, (1991)

[2] Joon on Yang: A Diagnostic Expert System for the Nuclear Power Plant Based on the Hybrid Knowledge Approach, IEEE Transaction on nuclear science,Vol.36,No.6,December,(1989)

[3] Zhao Peng, An Approach of Fault Diagnosis for System Based on Fuzzy Fault Tree, 2008 International Conference on Multimedia and Information Technology

DOI: 10.1109/mmit.2008.142

[4] Sohn, So Young; Moon, Tae Hee. Decision Tree based on data envelopment analysis for effective technology commercialization. Expert Systems with Applications，Vol, 26, Issue: 2, 2004, pp.279-284

DOI: 10.1016/j.eswa.2003.09.011

[5] L. P. Khoo, S. B. Tor and J. R. Li，A Rough Set Approach to the Ordering of Basic Events in a Fault Tree for Fault Diagnosis，the international journal of advanced manufacturing technology ,(2001) p.769–774

DOI: 10.1007/s001700170123

[6] W. hu, a. starr, z. zhou , a. leung, an intelligent integrated system scheme for machine tool diagnostics, the international journal of advanced manufacturing technology (2001) p.836～841

DOI: 10.1007/s001700170009

[7] Sheremetov, Leonid B.; Contreras, Miguel; Valencia, Cesar. Intelligent multi-agent support for the contingency management system. Expert Systems with Applications，Vol: 26, Issue: 1,2004, pp.57-71

DOI: 10.1016/s0957-4174(03)00107-6

[8] E W. Y. Zhang, S. B. Tor, G. A. Britton and Y.-M. Deng. EFDEX: A Knowledge-Based Expert System for Functional Design of Engineering Systems. Int J Adv Manuf Technol(2001),pp.339-353

DOI: 10.1007/s366-001-8300-2

[9] X. F. Zha, S. Y. E. Lim and S. C. Fok，Development of Expert System for Concurrent Product Design and Planning for Assembly，the international journal of advanced manufacturing technology (1999),p.153–162

DOI: 10.1007/s001700050052

[10] Burrell, Phillip and Inman, Dave. An expert system for the analysis of faults in an electricity supply network: problems and achievements,Computers in Industry,Vol 37, 1998,pp.113-123

DOI: 10.1016/s0166-3615(98)00087-6