In Auburngrads/SMRD: Statistical Methods For Reliability Data

SMRD:::vinny()
library(SMRD)

In this echapter...

Chapter overview

Resistor.rmd <- frame.to.rmd(resistor,
                             response.column = "percent",
                             time.column = "hours", 
                             unit.column = "resistor",
                             data.title = "Carbon-Film Resistor Accelerated Test", 
                             response.units = "Percent Increase in Resistance", 
                             x.columns = "celsius" )

## plot the degradation data

plot(Resistor.rmd)

plot(Resistor.rmd, y.axis = "log")

plot(Resistor.rmd, y.axis = "sqrt")

plot(Resistor.rmd, x.axis = "log")

plot(Resistor.rmd, 
     x.axis = "log",
     y.axis = "log",
     group.var = NA)

names(Resistor.rmd)

Resistor.ld1 <- rmd.to.ld(Resistor.rmd, 
                          fail.level = 5, 
                          subset = "hours > 0.6",
                          censor.time = 35,
                          x.axis = "sqrt")

SMRD:::plot.rmd.average(Resistor.rmd) #issue
SMRD:::plot.rmd.residual(Resistor.ld1) #issue

Resistor.ld <- rmd.to.ld(Resistor.rmd, 
                         fail.level = 5, 
                         subset = "hours" > 0.6,
                         censor.time = 35)

SMRD:::plot.rmd.residual(Resistor.ld)

trellis.plot(Resistor.rmd,
             order.groups = F,
             outer.plot = F)

trellis.plot(Resistor.rmd,
             order.groups = F,
             outer.plot = T,
             aspect = "fill")

## summarize the data

print(Resistor.ld) 
summary(Resistor.ld)

## analyze the Resistor pseudo failure-time data

censored.data.plot(Resistor.ld, 
                   x.axis = "log",
                   y.axis  = "Arrhenius")

groupi.mleprobplot(Resistor.ld, distribution = "Lognormal")

Resistor.groupm.out <- groupm.mleprobplot(Resistor.ld, 
                                          distribution = "Lognormal", 
                                          relationship  = "Arrhenius",
                                          ci.list = 1)

plot(Resistor.groupm.out,
     censor.time = 8000)

MetalWear example

MetalWear.rmd <- frame.to.rmd(metalwear,
                              response.column = 1,
                              time.column = 3,
                              unit.column = 2,
                              data.title = "Sliding Metal Wear",
                              x.columns = 4,
                              skip = 1)

plot(MetalWear.rmd)

plot(MetalWear.rmd, 
     x.axis="log",
     y.axis="log")

MetalWear.ld <- rmd.to.ld(MetalWear.rmd,
                          fail.level = 50,
                          ylim = c(2,100),
                          xlim = c(2,1000),
                          x.axis = "log",
                          y.axis = "log")

censored.data.plot(MetalWear.ld)

censored.data.plot(MetalWear.ld,
                   y.axis = "log", 
                   xlab = "Grams", 
                   ylab = "Cycles")

MetalWear.groupi.out <- groupi.mleprobplot(MetalWear.ld,
                                           distribution = "Lognormal")

MetalWear.groupm.out <- groupm.mleprobplot(MetalWear.ld,
                                           distribution = "lognormal",
                                           relationship = "class",
                                           ci.list = 1)

MetalWear.groupm.out <- groupm.mleprobplot(MetalWear.ld,
                                           distribution = "lognormal", 
                                           relationship = "linear", 
                                           ci.list = 1)

plot(MetalWear.groupm.out, 
     censor.time = 500)