This paper presents a methodology to calculate probabilities of failure using Probabilistic Support Vector Machines (PSVMs). Support Vector Machines (SVMs) have recently gained attention for reliability assessment because of several inherent advantages. Specifically, SVMs allow one to construct explicitly the boundary of a failure domain. In addition, they provide a technical solution for probl...

The computation of structural failure probability is vital importance in the reliability analysis and may be carried out on the basis of the first-order reliability method using various mathematical iterative approaches such as Hasofer-Lind and Rackwitz-Fiessler (HL-RF). This method may not converge in complicated problems and nonlinear limit state functions, which usually shows itself in the f...

Measurement errors and other uncertainties place an interval of ambiguity around the mean value of system probability of failure and each path and component failure probability. Associated with a specified reliability R that a failure probability will lie within an interval is a confidence C in that reliability. The statement that the system probability of failure lies within a specified interv...

due to the probabilistic nature and uncertainties of structural parameters, reliability-based optimization will enable engineers to account for the safety of the structures and allow for its decision making applicability. thus, reliability-based design will substitute deterministic rules of codes of practice. space structures are of those types that have an exceedingly high range of applicabili...

We derive an implementable algorithm for solving nonlinear stochastic optimization problems with failure probability constraints using sample average approximations. The paper extends prior results dealing with a failure probability expressed by a single measure to the case of failure probability expressed in terms of multiple performance measures. We also present a new formula for the failure ...

A system level fault injection experiment measuring changes in probability of failure and error propagation as a function of failure acceleration. It demonstrates that: (1) Probability of failure increases with failure acceleration, and (2) Error propagation decreases with failure acceleration .

Although traditional fault diagnosis methods can qualitatively identify the failure modes for power equipment, it is difficult to evaluate the failure probability quantitatively. In this paper, a failure probability calculation method for power equipment based on multi-characteristic parameters is proposed. After collecting the historical data of different fault characteristic parameters, the d...