drimmR (version 1.0.3)

maintainability: Maintainability function

Description

Maintainability of a system at time \(k \in N\) is the probability that the system is repaired up to time \(k\), given that is has failed at time \(k=0\).

Usage

maintainability(
  x,
  k1 = 0L,
  k2,
  upstates,
  output_file = NULL,
  plot = FALSE,
  ncpu = 2
)

Value

A vector of length k + 1 giving the values of the maintainability for the period \([0 \ldots k]\)

Arguments

x

An object of class dmm

k1

Start position (default value=0) : a positive integer giving the start position along the sequence from which the maintainabilities of the DMM should be computed, such that k1<k2

k2

End position : a positive integer giving the end position along the sequence until which the maintainabilities of the DMM should be computed, such that k2>k1

upstates

Character vector of the subspace working states among the state space vector such that upstates < s

output_file

(Optional) A file containing matrix of maintainability probabilities (e.g, "C:/.../MAIN.txt")

plot

FALSE (default); TRUE (display a figure plot of maintainability probabilities by position)

ncpu

Default=2. Represents the number of cores used to parallelized computation. If ncpu=-1, then it uses all available cores.

Author

Alexandre Seiller

Details

Consider a system (or a component) System whose possible states during its evolution in time are \(E = \{1 \ldots s \}\). Denote by \(U = \{1 \ldots s_1 \}\) the subset of operational states of the system (the upstates) and by \(D =\{s_{1}+1 \ldots s \}\) the subset of failure states (the down states), with 0 < s1 < s(obviously, \(E = U \cup D and U \cap D = \emptyset, U \neq \emptyset, D \neq \emptyset\)). One can think of the states of U as different operating modes or performance levels of the system, whereas the states of D can be seen as failures of the systems with different modes.

References

BaVe2018drimmR

See Also

fitdmm, getTransitionMatrix

Examples

data(lambda, package = "drimmR")
dmm <- fitdmm(lambda, 1, 1, c('a','c','g','t'),
init.estim = "freq", fit.method="sum")
k1 <- 1
k2 <- 200
upstates <- c("c","t")  # vector of working states
maintainability(dmm,k1,k2,upstates,plot=TRUE)