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The primary phase of electronic prognostic uncertainty quantification included the identification and quantification of uncertainty sources through utilizing sensitivity analysis method. An improved EFAST-based sensitivity analysis method that considered the possibility of parameter fluctuation was used to identify the key factors (KFS) of uncertainty sources. Also, an envelope probability method...
The uncertainties in prognostics have an effect on the applicability of prognostics methods, and the quality and the degree of trustiness of prognostics results. Monte Carlo method is the most common method for uncertainty analysis. But it is a time-consuming method and the simulation time consumed improves as the sampling times improve. This will cost a large amount of computing sources. In this...
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