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data-science/bearing_snippets/relabel.R
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# Read in best features | |
basedir <- "/Users/vic/Projects/bearings/bearing_IMS/1st_test/" | |
data <- read.table(file=paste0(basedir, "../all_bearings_best_fv.csv"), sep=",", header=TRUE) | |
# Read in the best kmeans model. Straight after running kmeans.R the | |
# filename will be "kmeans.obj", but this is the best model I found. | |
load(paste0(basedir, "../../models/best-kmeans-12-0.612.obj")) | |
## Adjust the class labels as a result of k-means | |
# Cluster 2 should be labelled "suspect" | |
data[best.kmeans$cluster==2,2] <- "suspect" | |
# Cluster 3 should be labelled "normal" | |
data[best.kmeans$cluster==3,2] <- "normal" | |
# Cluster 9 datapoints labelled "early" should be "normal" | |
data[((best.kmeans$cluster==9)&(data$State=="early")),2] <- "normal" | |
# b1 failure looks like a rolling element failure | |
data[data$State=="failure.b1", 2] <- "failure.roller" | |
data[((best.kmeans$cluster==11)&(data$State=="unknown")),2] <- "failure.roller" | |
data[((best.kmeans$cluster==11)&(data$State=="normal")),2] <- "suspect" | |
data[((best.kmeans$cluster==10)&(data$State=="unknown")),2] <- "early" | |
# Cluster 6 should all be "normal" | |
data[best.kmeans$cluster==6,2] <- "normal" | |
## Now plot to check the result | |
# use the same colours for states as before | |
cols <- do.call(rbind, Map(function(s) | |
{ | |
if (s=="early") "green" | |
else if (s == "normal") "blue" | |
else if (s == "suspect") "darkgoldenrod" | |
else if (s == "stage2") "salmon" | |
else if (s == "unknown") "black" | |
else "red" | |
}, data$State)) | |
# plot each bearing changing state | |
par(mfrow=c(2,2)) | |
for (i in 1:4) | |
{ | |
s <- (i-1)*2156 + 1 # 2156 datapoints per bearing | |
e <- i*2156 | |
plot(best.kmeans$cluster[s:e], col=cols[s:e], ylim=c(1,k), main=paste0("Bearing ", i), ylab="Cluster") | |
} | |
# Now save | |
write.table(data, file=paste0(basedir, "../all_bearings_relabelled.csv"), sep=",", row.names=FALSE) | |