Autoclub #4 Reliability - Refined
## An attempt to decipher Autoclub #4 Reliabilty and Availbility
## The Weibull distribution is commonly useed to model electronic/complex systems
## The lognormal distribution is commonly used to model the lives of units
## whose failure modes are of a fatigue-stress nature
## Sources: http://reliawiki.org and WeibullR for R
options(warnPartialMatchAttr = T)
rm(list = ls()) # remove all environment
source('~/CNC Matrix/itais_functions.R')
library(WeibullR)
Auto4 <- read.csv("Auto4 v3.csv",header = T, sep = ",", encoding = "UTF-8")
date <- "2015/01/01" # Installation / IOC date
q <- wblr(Auto4$Event, pch = 0)
q1 <- wblr.conf(wblr.fit(q, dist = "weibull2p"))
plot.wblr(q1, in.legend.blives = F)
q2 <- wblr.conf(wblr.fit(q, dist = "lognormal"))
plot.wblr(q2, in.legend.blives = F)
q3 <- wblr.fit(q, dist = "weibull3p")
plot.wblr(q3, in.legend.blives = F)
q4 <- wblr.fit(q, dist = "lognormal3p")
plot.wblr(q4, in.legend.blives = F)
f2(Auto4,"Auto-Club #4",MTTF = (q1$fit[[1]]$eta * gamma(1 / q1$fit[[1]]$beta + 1)))
## Auto-Club #4 Availability:
## Weibull MTTF 494.10344109066
## Events MTBF: 100.8
## Events MTTR: 2.54166666666667
## Events MLDT: 5.33333333333333
## Technical: 0.975
## Operational: 0.928
competeRisk(Auto4,"Auto#4 Competing Risks Models",10, date)
## bigIx start stop intercept slope adjR2
## 1 2 1 43 -0.06336831 0.0013004944 0.9684564
## 2 2 44 86 0.19851228 0.0007378657 0.9802455
## 3 3 1 29 -0.06979269 0.0013228537 0.9300008
## 4 3 30 58 0.09017063 0.0009080157 0.9802630
## 5 3 59 87 0.28795906 0.0006406744 0.9703825
## 6 4 1 22 -0.04480615 0.0011062997 0.9465798
## 7 4 23 44 0.04305539 0.0010163309 0.9698629
## 8 4 45 66 0.12404152 0.0008597494 0.9567787
## 9 4 67 88 0.35035673 0.0005765172 0.9746462
## 3P weibull:
## 3P log-normal: 3p optimization did not converge
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## 3P weibull:
## 3P log-normal: 3p optimization did not converge
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## [[1]]
## Eta Beta Rsqr AbPval
## 275.1353372 2.0953072 0.9719442 44.0193247
##
## [[2]]
## Eta Beta Rsqr AbPval
## 811.055204 4.760264 0.924675 5.571217
competeRisk(Auto4[25:56,],"Cooling",4, date)
## bigIx start stop intercept slope adjR2
## 1 2 1 16 8.076813e-05 0.0008988223 0.9712269
## 2 2 17 32 9.008077e-02 0.0009178956 0.9210596
## 3 3 1 11 -1.249582e-02 0.0009811482 0.9390980
## 4 3 12 22 -3.364810e-01 0.0016063355 0.9451575
## 5 3 23 33 2.936645e-01 0.0006819096 0.9105887
## 3P weibull:
## 3P log-normal:
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## 3P weibull:
## 3P log-normal:
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## [[1]]
## Eta Beta Rsqr AbPval
## 321.1492125 1.4770484 0.9553921 54.4048737
##
## [[2]]
## Eta Beta Rsqr AbPval
## 768.0882714 5.9273943 0.8789346 7.3496703
#M = f(time = Auto4$Event[25:56], title = "Cooling", pch = 4)
#f2(Auto4[25:56,],"Cooling", MTTF = M)
M = f(time = Auto4$Event[52:56], pch = 5, title = "Cooling (Valve events)", date)
## 3P weibull: 3p optimization did not converge
## 3P log-normal: Bad log-normal 3p solution
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[34:51,],"Cooling (Pump Flange)", MTTF = M)
## Cooling (Pump Flange) Availability:
## Weibull MTTF 391.621826499677
## Events MTBF: 430.117647058824
## Events MTTR: 2
## Events MLDT: 0
## Technical: 0.995
## Operational: 0.995
M = f(time = Auto4$Event[26:28], pch = 8, title = "Cooling (Fan Events)", date)
## 3P weibull: Bad weibull 3p solution
## 3P log-normal: Bad log-normal 3p solution
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[26:28,],"Cooling (Fan)", MTTF = M)
## Cooling (Fan) Availability:
## Weibull MTTF 732.827157702875
## Events MTBF: 1624
## Events MTTR: 5.1666666665
## Events MLDT: 0
## Technical: 0.997
## Operational: 0.997
M = f(time = Auto4$Event[8:11], title = "Bridge", pch = 1, date)
## 3P weibull: 3p optimization did not converge
## 3P log-normal: Bad log-normal 3p solution
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[8:11,],"Bridge", MTTF = M)
## Bridge Availability:
## Weibull MTTF 804.414407535274
## Events MTBF: 1690.66666666667
## Events MTTR: 2
## Events MLDT: 0
## Technical: 0.999
## Operational: 0.999
competeRisk(Auto4[12:24,],"Control",2, date)
## bigIx start stop intercept slope adjR2
## 1 2 1 6 -0.4000840 0.003519469 0.8093661
## 2 2 7 12 0.4403568 0.000495850 0.9297100
## 3P weibull: Bad weibull 3p solution
## 3P log-normal: Bad log-normal 3p solution
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## 3P weibull:
## 3P log-normal:
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## [[1]]
## Eta Beta Rsqr AbPval
## 196.9055959 4.8978315 0.9049908 38.1180147
##
## [[2]]
## Eta Beta Rsqr AbPval
## 566.1904426 1.8852415 0.9165406 45.3769880
##
## [[3]]
## [1] 0
#M = f(time = Auto4$Event[12:24], title = "Control", pch = 2)
#f2(Auto4[12:24,],"Control", MTTF = M)
M = f(time = Auto4$Event[16:24], title = "Control (Electric Events)", pch = 3, date)
## 3P weibull:
## 3P log-normal:
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[16:24,],"Control (Electric)", MTTF = M)
## Control (Electric) Availability:
## Weibull MTTF 546.106546273875
## Events MTBF: 836
## Events MTTR: 1.75
## Events MLDT: 24
## Technical: 0.998
## Operational: 0.97
competeRisk(Auto4[57:72,],"Door",9, date)
## bigIx start stop intercept slope adjR2
## 1 2 1 8 -0.2081153 0.0019791847 0.8919147
## 2 2 9 16 0.3735876 0.0005584848 0.9054128
## 3P weibull: Bad weibull 3p solution
## 3P log-normal: Bad log-normal 3p solution
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## 3P weibull:
## 3P log-normal:
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## [[1]]
## Eta Beta Rsqr AbPval
## 262.6245764 3.0933668 0.9297024 48.5634262
##
## [[2]]
## Eta Beta Rsqr AbPval
## 736.3617579 3.0680041 0.8654491 15.2647100
#M = f(time = Auto4$Event[57:72], pch = 9, title = "Door Events")
#f2(Auto4[57:72,],"Door", MTTF = M)
M = f(time = Auto4$Event[57:59], pch = 10, title = "Door Mechanism Events", date)
## 3P weibull:
## 3P log-normal:
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[57:59,],"Door Mechanism", MTTF = M)
## Door Mechanism Availability:
## Weibull MTTF 274.121281364708
## Events MTBF: 1240
## Events MTTR: NaN
## Events MLDT: NaN
## Technical: NaN
## Operational: NaN
M = f(time = Auto4$Event[60:65], pch = 11, title = "Door Micro-Switch Events", date)
## 3P weibull:
## 3P log-normal:
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[60:65,],"Door Micro-switch", MTTF = M)
## Door Micro-switch Availability:
## Weibull MTTF 446.17341328319
## Events MTBF: 894.4
## Events MTTR: NaN
## Events MLDT: NaN
## Technical: NaN
## Operational: NaN
M = f(time = Auto4$Event[66:72], pch = 12, title = "Door Seal Events", date)
## 3P weibull:
## 3P log-normal:
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[66:72,],"Door Seal", MTTF = M)
## Door Seal Availability:
## Weibull MTTF 621.316331350552
## Events MTBF: 1162.66666666667
## Events MTTR: 1.7083333335
## Events MLDT: 0
## Technical: 0.999
## Operational: 0.999
M = f(time = Auto4$Event[73:76], title = "Heating System", pch = 13, date)
## 3P weibull:
## 3P log-normal:
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[73:76,],"Heating System", MTTF = M)
## Heating System Availability:
## Weibull MTTF 541.1252502926
## Events MTBF: 1176
## Events MTTR: NaN
## Events MLDT: NaN
## Technical: NaN
## Operational: NaN
M= f(time = Auto4$Event[77:82], pch = 14, title = "Nitrogen System Events", date)
## 3P weibull: Bad weibull 3p solution
## 3P log-normal: Bad log-normal 3p solution
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[77:82,],"Nitrogen System", MTTF = M)
## Nitrogen System Availability:
## Weibull MTTF 660.958299872334
## Events MTBF: 1265.6
## Events MTTR: 3
## Events MLDT: 4
## Technical: 0.998
## Operational: 0.994
M = f(time = Auto4$Event[83:86], pch = 15, title = "Vacuum System Events", date)
## 3P weibull: Bad weibull 3p solution
## 3P log-normal: Bad log-normal 3p solution
## Weibull 2-paprameters and 3-parameters likelyhood
## Log-normal 2-paprameters and 3-parameters likelyhood
## Failure prediction
f2(Auto4[83:86,],"Vacuum System", MTTF = M)
## Vacuum System Availability:
## Weibull MTTF 270.386675505244
## Events MTBF: 877.333333333333
## Events MTTR: NaN
## Events MLDT: NaN
## Technical: NaN
## Operational: NaN