Notice of Violation of IEEE Publication Principles: Reliability Estimation and Empirical Research for Series-Parallel Systems

Notice of Violation of IEEE Publication Principles

"Reliability Estimation and Empirical Research for Series-Parallel Systems,"
by Dawei Liu
in the Proceedings of the ISECS International Colloquium on Computing, Communication, Control, and Management, 2008. (CCCM '08), vol.3, pp.375-379, 3-4 Aug. 2008

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Complex System Reliability Estimation and Optimization Considering Component Estimation Uncertainties,"
by Tongdan Jin
in Chapter 3 of Dr. Jin's Ph.D. dissertation, Rutgers University, 2001


System reliability can be improved through redundancy by allocating additional components in parallel so the system is creditable even if one or more components fail. Often, the parallel parts are repeated components of the same type. These components usually share the same reliability estimation. Thus, the components reliability estimates are statistically dependent. Various non-parametric models for the system reliability estimation have been discussed a lot. Current reliability estimation models, such as the extended lognormal model and modified maximum likelihood model can partially accommodate the component repetition at the system level, yet in a very limited manner. Therefore, a new reliability estimation model has been proposed to overcome the difficulties of traditional non-parametric estimation models. Based on an approximation approach, the new model provides accurate estimation for the mean and the variance of system reliability estimate when repeated components are used anywhere in the system.