Monte Carlo-based filtering for fatigue crack growth estimation

143 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Cloud-enabled prognosis for manufacturing (2015, background): “Similar work can be found for the fatigue crack dynamic evolution of a mechanical component [147-148].”
Particle filtering prognostic estimation of the remaining useful life of nonlinear components (2011, methodology): “In the case of non-destructive ultrasonic inspections, a logit model for the observation zk can be introduced [17]: ln zk d zk 1⁄4 b0þb1 ln xk d xk þnk ð16Þ”
Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks (2017, methodology): “Following the approach proposed in [28], at the current time step 𝑘 the posterior pdf of the end of discharge time (i.e. EOD 𝑘 ) is estimated by projecting in the future the predictions of the 𝑁 𝑠 RBF networks associated to the 𝑁 𝑠 parameter samples (particles) 𝒙 𝑘(𝑖) ) become lower than…”