Dear Bugs Users,
I am modeling a bearing lifetime in WinBUGS 1.4.1.
There are 5 units on test. They are told by the bearing manufacturer that the bearings should last 100 hours (L10 life) in this application. L10 life means the time at which 10 % of the population fail. Thus far one unit has failed at 40 hours, one at 8 hours. One unit has 13 hours with no failures, one has 15 hours with no failures, and one has 1 hour with no failures. I'm trying to set up a Bayesian updating model that calculates the probability of the bearing life falling within a given range. An initial distribution of equal probabilities is suggested that the bearing life is 20-40 hours, 40-60 hours, 60-80 hours, 80-100 hours, and 100-120 hours.
I'm not sure the model below is reasonable. Can anyone give some comments to me?
Thanks in advance,
Yoonik.
model
{
for(i in 1 : M) {
for(j in 1 : N) {
t[i, j] ~ dexp(lambda.new[i])I(t.cen[i, j],)
}
lambda[i] ~ dunif(0, 949.1222)
lambda.new[i] <- lambda[i]
life[i] <- 1/lambda.new[i]
}
}
list(t = structure(.Data =
c(40, 8, NA, NA, NA),
.Dim = c(1, 5)),
t.cen = structure(.Data =
c( 0, 0, 13, 15, 1),
.Dim = c(1, 5)),
M = 1, N = 5)
list(lambda = c(
0.00105))
--
---------------------------------
Yoonik Kim, Ph.D.
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