informative_masking_by_rank

Returns m=length(ts) probabilities, where the j-th probability is the probability that the j-th component will be in the candidate set. probabilities are a function of rank (rather than by component failure times).

Usage

informative_masking_by_rank(ts, alpha, beta)

Arguments

ts

component failure times for the series system

alpha

rate of change with respect to rank, as alpha -> 0 probabilities go to discrete uniform distribution

beta

max weight (for ranked 2 component)

Details

The shape of the masking is given by two parametrs, alpha, which must be non-negative, and beta, which must be between 0 and 1. As alpha goes to 0, the probability vector approaches (beta, beta, ..., 1, beta, ..., beta) where the 1 is for the failed component and as alpha goes to infinity, it assigns probability 1 and beta respectively to rank 1 (failed component) and rank 2 components (and the remaining probabilities are zero).