Title
POF-Darts: Geometric adaptive sampling for probability of failure.
Abstract
We introduce a novel technique, POF-Darts, to estimate the Probability Of Failure based on random disk-packing in the uncertain parameter space. POF-Darts uses hyperplane sampling to explore the unexplored part of the uncertain space. We use the function evaluation at a sample point to determine whether it belongs to failure or non-failure regions, and surround it with a protection sphere region to avoid clustering. We decompose the domain into Voronoi cells around the function evaluations as seeds and choose the radius of the protection sphere depending on the local Lipschitz continuity. As sampling proceeds, regions uncovered with spheres will shrink, improving the estimation accuracy. After exhausting the function evaluation budget, we build a surrogate model using the function evaluations associated with the sample points and estimate the probability of failure by exhaustive sampling of that surrogate. In comparison to other similar methods, our algorithm has the advantages of decoupling the sampling step from the surrogate construction one, the ability to reach target POF values with fewer samples, and the capability of estimating the number and locations of disconnected failure regions, not just the POF value. We present various examples to demonstrate the efficiency of our novel approach.
Year
DOI
Venue
2016
10.1016/j.ress.2016.05.001
Reliability Engineering & System Safety
Keywords
Field
DocType
Probability of failure,Percentile estimation,Reliability,Computational geometry,Surrogate models
Computer science,Adaptive sampling,Surrogate model,Sampling (statistics),Voronoi diagram,Lipschitz continuity,Parameter space,Hyperplane,Statistics,Cluster analysis,Reliability engineering
Journal
Volume
ISSN
Citations 
155
0951-8320
0
PageRank 
References 
Authors
0.34
5
5
Name
Order
Citations
PageRank
Mohamed S. Ebeida115311.28
Scott A. Mitchell246177.77
Laura P. Swiler3564.35
Vicente J. Romero480.90
Ahmad Rushdi5304.62