ttt | R Documentation |
ttt
calculates the scaled total time on test (TTT) as described in
Tests for trend in more than one repairable system
and Analysis of Time Between Failures for Repairable Components
As indicated in Analysis of Time Between Failures for Repairable Components
this can be used to create a TTT plot.
A TTT plot that follows the line y = x
indicates a Homogeneous Poisson Process (HPP) (constant failure rate),
while a concave TTT plot indicates a Nonhomogeneous Poisson Process (NHPP).
Concave up indicates decreasing failure rate,
while concave down indicates increasing failure rate. See:
Minitab TTT Plot
ttt(t, T, fail.trunc = FALSE)
t |
A list of failure time vectors. Each vector should indicate a different system, i.e. if you have multiple systems each systems' failure times should be in it's own vector. |
T |
A list of Total Time on Test (TTT) (i.e. test duration) vectors. The vectors in the list should be of length 1, and each vector should indicate a different system, i.e. if you have multiple systems each systems' TTT should be in it's own vector. |
fail.trunc |
Logical indicating if the test was failure terminated. |
The output will be a data.frame with the sorted supplied time
values (t
), the total time on test (ttt
), and the
scaled total time on test (scaled_ttt
).
A plot of scaled_ttt
vs ttt
would be a TTT plot.
power_law_process
, power_law_mcf
,
mcf
, trend_test
, common_beta
data(amsaa)
# Three systems all time truncated at 200 hours
ttt_df <- ttt(
t = split(amsaa$Time, amsaa$System),
T = list(200,200,200),
fail.trunc = FALSE)
theme_set(theme_bw())
ggplot(ttt_df, aes(x = ttt, y = scaled_ttt)) +
geom_line(colour = "red") + geom_point() +
geom_abline(intercept = 0, slope = 1) +
labs(
x = "Total Time on Test",
y = "Scaled Total Time on Test")
rm(list = c("amsaa", "ttt_df"))
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