maintainability {smmR} | R Documentation |
Maintainability Function
Description
For a reparable system for which the failure
occurs at time
, its maintainability at time
is
the probability that the system is repaired up to time
, given that
it has failed at time
.
Usage
maintainability(x, k, upstates = x$states, level = 0.95, klim = 10000)
Arguments
x |
An object of S3 class |
k |
A positive integer giving the period |
upstates |
Vector giving the subset of operational states |
level |
Confidence level of the asymptotic confidence interval. Helpful
for an object |
klim |
Optional. The time horizon used to approximate the series in the
computation of the mean sojourn times vector |
Details
Consider a system (or a component) whose possible
states during its evolution in time are
.
Denote by
the subset of operational states of
the system (the up states) and by
the
subset of failure states (the down states), with
(obviously,
and
,
). One can think of the states
of
as different operating modes or performance levels of the
system, whereas the states of
can be seen as failures of the
systems with different modes.
We are interested in investigating the maintainability of a discrete-time
semi-Markov system . Consequently, we suppose that the
evolution in time of the system is governed by an E-state space
semi-Markov chain
. The system starts to fail at
instant
and the state of the system is given at each instant
by
: the event
, for a certain
, means that the system
is in operating mode
at time
, whereas
, for a certain
, means that the system is not operational at time
due to the mode of failure
or that the system is under the
repairing mode
.
Thus, we take and we
denote by
the first hitting time of subset
, called the
duration of repair or repair time, that is,
The maintainability at time of a discrete-time semi-Markov
system is
Value
A matrix with rows, and with columns giving values of
the maintainability, variances, lower and upper asymptotic confidence limits
(if
x
is an object of class smmfit
).
References
V. S. Barbu, N. Limnios. (2008). Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications - Their Use in Reliability and DNA Analysis. New York: Lecture Notes in Statistics, vol. 191, Springer.