Simulate Failures via PPS Sampling

Simulates which units in a non-failed population fail next using probability proportional to size (PPS) sampling based on unit runtimes. Units with longer runtimes have a proportionally higher probability of being selected. 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)

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 runtime + Uniform(0, window) and suspension times are runtime + window.

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.

See also

Other data preparation: weibull_to_rga()

Examples

set.seed(42)
runtimes <- c(100, 500, 200, 800, 300)
result <- sim_failures(2, runtimes)
print(result)
#>   index runtime       type
#> 1     1     100 Suspension
#> 2     3     200    Failure
#> 3     5     300    Failure
#> 4     2     500 Suspension
#> 5     4     800 Suspension

# With an observation window
set.seed(42)
result_w <- sim_failures(2, runtimes, window = 50)
print(result_w)
#>   index  runtime       type
#> 1     1 150.0000 Suspension
#> 2     3 214.3070    Failure
#> 3     5 341.5224    Failure
#> 4     2 550.0000 Suspension
#> 5     4 850.0000 Suspension