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Notice of Violation of IEEE Publication Principles
"Decision Trees Generation Based on Fault Trees Analysis,"
by Yongjian Tao, Decun Dong, Peng Ren,
in the Proceedings of the 2009 International Forum on Information Technology and Applications, 2009, August 2009, pp. 178-180
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 portions of text from the paper(s) cited below. A credit notice is used, but due to the absence of quotation marks or offset text, copied material is not clearly referenced or specifically identified.
"Minimal Cut Set/Sequence Generation for Dynamic Fault Trees,"
by Zhihua Tang; Dugan, J.B.
in the Proceedings of the 2004 Annual Symposium Reliability and Maintainability, Jan. 2004 pp. 207- 213
"Diagnostic Expert Systems from Dynamic Fault Trees,"
by Assaf, T., Dugan, J.B.
in the Proceedings of the 2004 Annual Symposium Reliability and Maintainability, Jan. 2004, pp. 444- 450
This paper proposes a fault tree analysis method to generate diagnostic decision trees. All minimal cut sets, their occurrence probabilities and components' diagnosis importance factors are determined via fault tree analysis used for system reliability. Minimal cut sets represent minimal sets of component failures that cause a system failure, so the diagnostic sequence of minimal cut sets should be first assigned. Minimal cut sets with larger occurrence probabilities are diagnosed first. The order by which components within each minimal cut set are depend on the diagnostic importance factor ordering; components of larger diagnosis importance factor are checked first. Moreover, if the component which has the largest diagnosis importance factor within the minimal cut set being checked is failed, the minimal cut sets including it are also diagnosed first- . According to diagnostic sequence of system components, a diagnostic decision tree can be generated. This method avoids deficiency caused by the diagnostic sequence which is merely determined by the diagnosis importance factors of components, regardless of occurrence probabilities of minimal cut sets. This paper presents an example to demonstrate efficiency of this method.