The input of the module is a csv file. The file contains an unordered dataset, with provided information of engine inspections since 2004 for each engine carbon brush. In case carbon brush was replaced due to deterioration - the event is signed by “1”, otherwise by “0”.
The outputs of the module are convenient for further work datasets based on the provided raw data.
## Cement Mill 10 (January 2004 - February 2018)
## Maintaneince days summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 24.00 27.00 30.05 29.00 525.00
##
## Maintaneince days Outleirs:
## lowerFarOut lowerOutliers upperOutliers upperFarOut
## 9.0 16.5 36.5 44.0
##
## Replacement periods summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 138.2 270.5 328.3 416.0 1965.0
##
## Replacement periods upperOutliers:
## upperOutliers upperFarOut
## 832.625 1249.250
Low “FarOut” Actually, low farOuts points (days of carbon brush replacement) mean that carbon brushes were replaced on the same day or after few days. In both cases, low farOuts might be entered same information twice- and must be removed from dataset.
## Mill C R brushName brushType phase fromDate toDate Days
## 99 CM10 1 6 C1R06 CM-2 U 2018-01-14 2018-01-15 1
## 150 CM10 1 10 C1R10 CM-2 U 2018-01-14 2018-01-15 1
Here, it is easy to find out places, where carbon brushes were replaced significantly more frequently than a majority of carbon brushes of this engine. Presumably, these carbon brushes were affected by local factors (like a local clamping spring) more than common reasons (for this engine).
This chart provides information about “frozen” (rarely replaced) carbon brushes. Probably “frozen” brushes have a relatively weak clamping spring - as result, the carbon brush, apparently, did not do the job at all. -For example carbon brush C1R2 and C3R6 - were not deteriorated more than five years (The criterion for replacement is the size (deterioration) of the carbon brush).
We should take into account, that there are upper outliers in the maintenance list - the reason for extremely long intervals between maintenance is probably data loss of maintenance cases:
## dateDiff fromtDate toDate
## 1 40 2004-04-13 2004-05-23
## 2 37 2006-02-15 2006-03-24
## 3 42 2012-07-17 2012-08-28
## 4 44 2013-04-14 2013-05-28
## 5 525 2013-12-22 2015-05-31
## 6 152 2016-06-01 2016-10-31
## 7 44 2017-02-05 2017-03-21
## 8 37 2017-03-21 2017-04-27
## 9 97 2017-04-27 2017-08-02
There is a chance that deteriorated carbon brushes had been replaced during those periods, but we have no information - and as result, we see the lifespan of them as outliers:
## brushName fromDate toDate Days
## 1 C1R02 2010-06-20 2015-07-28 1864
## 2 C1R10 2013-04-14 2015-10-18 917
## 3 C2R01 2010-04-28 2013-12-15 1327
## 4 C2R02 2010-03-14 2013-04-14 1127
## 5 C2R04 2009-05-24 2011-10-10 869
## 6 C2R05 2009-05-24 2011-10-10 869
## 7 C2R07 2009-01-15 2011-10-10 998
## 8 C3R01 2008-12-18 2013-01-16 1490
## 9 C3R02 2010-03-14 2013-04-14 1127
## 10 C3R02 2013-12-15 2017-04-27 1229
## 11 C3R03 2009-07-12 2012-08-28 1143
## 12 C3R04 2010-08-19 2013-01-16 881
## 13 C3R05 2009-07-12 2013-04-14 1372
## 14 C3R06 2010-04-06 2015-08-23 1965
## 15 C3R07 2009-12-30 2012-08-28 972
## 16 C3R08 2009-12-30 2012-08-28 972
## 17 C3R08 2013-07-22 2016-01-11 903
## 18 C3R09 2010-04-06 2013-04-14 1104
## 19 C3R10 2013-07-22 2015-11-15 846
In order to exclude the uncertainty- whether carbon brushes lifespan outliers are true, or results of information loss- we can test which of these time periods are overlapping (outliers of periods between maintenance and outliers of periods between carbon brushes replacement).
According to the table, except of C2R04, C2R05, C2R07 other lifespan outliers might be a result of the lost information. For example, it looks like carbon brush C3R06 was not replaced for about five years, but this period is overlapped with three periods of lost information, during which carbon brushes might have been replaced for several times.
For sure, we can only say, that C2R04, C2R05 carbon brushes were not replaced more then two years, and C2R07 for about three years. Except for those three cases, other 16 outliers are better to be removed from the dataset.
“Importance” (to put attention) provides visual information about the number of days since last replacement- lower than second quintile, or upper then third quintile. The importance is calculated by the following principles: Maximum value - 10, is equivalent to the maximum distance from the second quantile down (for the low values -“burned” brushes) and from the third quantile up (for the upper values- “frozen” brushes) found in the whole engine history. The minimum value “1” - the number of days since last replacement, lays in the second or third quantiles.
Colors: green- the number of days since last replacement lays in third or second quintile;
red- “burned” brush - the number of days since last replacement and lifetime of the previous brush in this place lay in the first quintile;
blue- “frozen” brush- the number of days since last replacement lays in the fourth quintile.