Fault Diagnosis and Reliability Growth for Information System

Abstract:

This paper aiming at the zero-failure data and uncertain-decision problems exist in the information system reliability growth process, it proposes to build the Bayesian network topology of FMEA. It adopts Leaky Noisy-OR model, and it analyses the probability that the subsystem functional module will go wrong in quantity. It solves the problem of identifying the vague and incomplete information exists in the complex system rapidly and accurately, laying the foundation for further study of the reliability growth comprehensive ability assessment of system based on the Bayesian network. In this paper, on the background of Manufacturing Execution Systems (MES) engineering, aimed at research on models and evaluation methods of reliability growth for MES, enclosing reliability of MES task and design target, reliability growth test and analysis methods, it proposes the goal of MES reliability growth planning.

• Periodical: Applied Mechanics and Materials (Volume 590)
• Pages: 763-767
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.590.763
• Online since: June 2014
• Authors: Zhi Hui Huang*
• Keywords: Bayesian Network (BN), Efficiency Improvement Modelling, Information System, Reliability Growth

References

[1] Seddon, P. B. A Respecification and Extension of the DeLone and McLean Model of IS Success, Information Systems Research [J]. 1997, 8(3): 240-253.

DOI: 10.1287/isre.8.3.240

[2] Ballantine, J., Bonner M., et al. The 3-D Model of Information System Success: The Search for Dependent Variable Continues. Information Resources Management [J], 1996, 9(4): 5-14.

DOI: 10.4018/irmj.1996100101

[3] Rai, A., Lang, S., Welker, R., Assessing the Validity of IS Success Models: An Empirical Test and Theoretical Analysis, Information Systems Research [J], 2002, 13(1): 50-69.

DOI: 10.1287/isre.13.1.50.96

[4] li qing, wang yunwu, Liang Chao. Manufacturing execution system implementation of the party ability to implement comprehensive evaluation technology research [J], manufacturing automation, 2007, 47 (1) : 1-6.

[5] Agnieszka Onisko , Marek J. Druzdzel , Hanna Wasyluk, Learning Bayesian Network Parameters from Small Data Sets: Application of Noisy-OR Gates[J], INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2000, pp.165-182.

DOI: 10.1016/s0888-613x(01)00039-1

[6] Tomas Singliar, Milos Hauskrecht, Noisy-OR Component Analysis and its Application to Link Analysis[J], Journal of Machine Learning Research, 2006: 2189-2213.