Sentence examples for prognostic framework from inspiring English sources

A multiple damage-mode model for the estimation of the strain energy release rate and the remaining stiffness of damaged laminates constitutes the core of the particle filtering algorithm, thus allowing the prognostic framework to extend for monitoring of simultaneous, coexisting damages.

In this study, the CMTC prognostic framework with 12 microarray-based gene signatures [ 9- 20] was reproduced in the internal validation cohort with 284 breast cancers.

Prognostic frameworks based on Bayesian processors, such as particle filtering, have already demonstrated their efficiency when trying to estimate the probability of failure in nonlinear, non-Gaussian, systems with stochastic operating profiles.

This fact has encouraged the development of a series of failure prognostic frameworks based on Bayesian processors (e.g. particle or unscented Kalman filters), which efficiently help to estimate the Time-of-Failure (ToF) probability distribution in nonlinear, non- Gaussian, systems with uncertain future operating profiles.

Section 2 introduces the integrated prognostics framework that gives a global view of the structure of the proposed method.

An integrated prognostics framework is proposed in this section, and we use gears with fatigue crack as an example to present the proposed method.

Therefore, different aspects crucial to the prognostics framework, i.e., from monitoring data to remaining useful life of equipment need to be addressed.

A shock causes a sudden damage increase and creates a jump in the degradation path, which shortens the total lifetime, and it has not been considered before in the integrated prognostics framework either.

A shock causes a sudden damage increase and creates a jump in the degradation path, which shortens the total lifetime, and it has not been considered before either in the integrated prognostics framework.

The current prognostic model, the PREDICT model, provides a framework for prognostications and risk adjustment when long-term survival of critically ill patients is considered.

This paper contributes to the growing body of research in RST and its extensions as a prognostic modeling framework and highlights the strengths of this approach in terms of accessibility.