Distributed Fault Detection System Design Based on the Communication Protocol

Zhang Peng; You  Hua Ding; Hai Qiong Guo

doi:10.4028/www.scientific.net/AMM.427-429.742

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429Distributed Fault Detection System Design Based on...

Distributed Fault Detection System Design Based on the Communication Protocol

Abstract:

This article analyze the transmitting characteristics of information in the modern And than, we find the basic design criterion of fault detection system in the complicated electronic devices. Than we discuss the possibility of achieving fault detection through a communication protocol. So we describe the design project of achieving detecting fault function through the communication protocol. At last, we brief introduce simulative detection function about fault detection circuit.

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Periodical:

Applied Mechanics and Materials (Volumes 427-429)

Pages:

742-745

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.427-429.742

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Online since:

September 2013

Authors:

Zhang Peng*, You Hua Ding, Hai Qiong Guo

Keywords:

Fault Detection, Protocol, Simulation, System Design

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* - Corresponding Author

References

[1] Dale W. Richards, James A. Collins. Intelligent Built-in-Test and Stress Measurement. IEEE AUTOTESTCON. 1999, 261~266.

Google Scholar

[2] Zourides V. G. Smart Built-in-Test an Overview. IEEE AUTOTESTCON. 1989, 417～421.

Google Scholar

[3] Dale W R. SMART BIT: A Plan for Intelligent Built-in Test. IEEE AUTOTESTCON. 1990，1230～1233.

Google Scholar

[4] Carpenter G., Grossberg S. ART-2 A: an adaptive algorithm for rapid category learning and recognition. Nerual Network. 2001, 4(6): 493～504.

Google Scholar

[5] Ho T. K., Hull J. J., Srihari S N. Decision combination in multiple classifier system. IEEE Trans on PAMI, 1994, 16(1), 66～75.

DOI: 10.1109/34.273716

Google Scholar

[6] Gallant Stephen I．Neural Network Learning and Expert Systems．I Cambridge, Massachusetts, London, England The MIT Press 2010, 195~198.

Google Scholar

[7] A.P., Brits R．A clustering approach to incremental learning for feedforward neural networks Engel-brecht, Neural Networks, 2001．Proceedings. IJCNN'01．International Joint Conference on, 2001, 3: 2019~(2024).

DOI: 10.1109/ijcnn.2001.938474

Google Scholar

[8] A. Cha, H. M. RF Built-in Test and Enabling Technologies for Integrated Diagnostics. Proc of NAEC. 2005: 207～211.

Google Scholar

[9] V. Szekeiy, M. Rencz. Thermal Monitoring of Self-Checking Systems. Journal of Electronic Testing: Theory and Application. 1998 (12), 81-92.

Google Scholar

[10] Erika F. Cota, Marcelo Negreiros, Luigi Carro and Marcelo Lubaszewski. A New Adaptive Analog Test and Diagnosis System. IEEE Transaction on Instrumentation and Measurement, 2000, 49(2).

DOI: 10.1109/19.843053

Google Scholar