sim_failures: Simulate Failures from a Conditional Weibull Model

Description

Simulates which units in a non-failed population fail next by using a Weibull life model conditional on each unit's current runtime. When a positive window is supplied, the function calibrates a Weibull scale parameter (unless provided directly) so that the expected number of failures within the window matches n. Units are then sampled with probability proportional to their conditional Weibull failure probability over the window, and failure times are drawn from the truncated conditional Weibull distribution. The full fleet is returned: selected units are labelled "Failure" and the remaining units are labelled "Suspension".

Usage

sim_failures(n, runtimes, replace = FALSE, window = NULL, beta = 1, eta = NULL)

Value

A data frame with length(runtimes) rows sorted ascending by runtime, containing:

index: Integer index of the unit in runtimes.
runtime: Simulated event time.
type: Character; "Failure" for selected units, "Suspension" for the rest.

The returned object also carries attributes weibull_beta and weibull_eta describing the Weibull parameters used for the simulation.

Arguments

n: Positive integer. Number of failures to simulate.
runtimes: Numeric vector of positive values. The current operating runtime of each unit in the non-failed population.
replace: Logical scalar. If TRUE, sampling is done with replacement (a unit may be selected more than once). Default is FALSE.
window: NULL or a single positive numeric. The width of the observation window. When NULL (default), event times equal current runtimes. When provided, failure times are sampled from the conditional Weibull distribution over (runtime, runtime + window], and suspension times are runtime + window.
beta: Positive numeric scalar. Weibull shape parameter used to model the age-dependent hazard. Defaults to 1, corresponding to an exponential-process assumption.
eta: NULL or a single positive numeric. Weibull scale parameter. When NULL and window is supplied, the scale is calibrated so that the expected number of failures across the fleet during the window matches n.

Details

When window = NULL, the function returns the current fleet state at the supplied runtimes. In this case, failing units are selected using relative Weibull hazard weights implied by beta.

Examples

Run this code

set.seed(42)
runtimes <- c(100, 500, 200, 800, 300)
result <- sim_failures(2, runtimes, beta = 1.5)
print(result)

# With an observation window
set.seed(42)
result_w <- sim_failures(2, runtimes, window = 50, beta = 1.5)
print(result_w)