Complex system reliability evaluation using support vector machine

Projekt:

JVTC

Sammanfattning:
Support Vector Machine (SVM) is a data mining technique that has been successfully
used in classification problems, starting from a known training data set (TDS). In systems
modeled as networks, SVM has been used to classify the state of a network as failed or
operating and jointly combined in a Monte Carlo sampling approach to approximate the
network reliability. The analytical expression of the binary function (failed/operating)
produced by SVM is difficult to be understood, since it generally involves the evaluation of
non-linear operators, which consider a subset of the TDS, called Support Vectors (SV) and
sampled system states. In this paper a different approach is proposed to assess system
reliability. Information about path and cut sets is obtained directly from SV, without
considering the analytical expression of the binary function produced by SVM. From here the
system reliability is approximated directly. Several examples illustrate the approach.

Författare: Yuan Fuqing ; Uday Kumar ; Claudio M. Rocco S ; Krishna B. Misra
Utgivare: International Symposium on Stochastic Models in Reliability Engineering, Life Sciences and Operations Management
Utgivningsdatum: 2010
Diarienummer: TRV 2011/58769
Språk: Engelska
Kontaktperson: Per Olof Larsson Kråik, UHjbs