Predicting Car Battery Failure With R And H2O – Study

May 24, 2019
By

(This article was first published on R-Analytics, and kindly contributed to R-bloggers)

# Loading libraries
suppressWarnings( suppressMessages( library( h2o ) ) )
suppressWarnings( suppressMessages( library( data.table ) ) )
suppressWarnings( suppressMessages( library( plotly ) ) )
suppressWarnings( suppressMessages( library( DT ) ) )

# Reading data file
# Data from: https://www.kaggle.com/yunlevin/levin-vehicle-telematics
dataFileName = "/Development/Analytics/AnomalyDetection/AutomovileFailurePrediction/v2.csv"
carData = fread( dataFileName, skip=0, header = TRUE )
carBatteryData = data.table( TimeStamp = carData$timeStamp
, BatteryVoltage = as.numeric( carData$battery )
)
rm(carData)

# Data cleaning, filtering and conversion
carBatteryData = na.omit( carBatteryData ) # Keeping just valid Values

# According to this article:
# https://shop.advanceautoparts.com/r/advice/car-maintenance/car-battery-voltage-range
#
# A perfect voltage ( without any devices or electronic systems plugged in )
# is between 13.7 and 14.7V.
# If the battery isn’t fully charged, it will diminish to 12.4V at 75%,
# 12V when it’s only operating at 25%, and up to 11.9V when it’s completely discharged.
#
# Battery voltage while a load is connected is much slower
# it should be something between 9.5V and 10.5V
#
# This value interval ensures that your battery can store and deliver enough
# current to start your car and power all your electronics and electric devices
# without any difficulty

carBatteryData = carBatteryData[BatteryVoltage >= 9.5] # Filtering voltages greater or equal to 9.5
carBatteryData$TimeStamp = as.POSIXct( paste0( substr(carBatteryData$TimeStamp,1,17),"00" ) )
carBatteryData = unique(carBatteryData) # Removing duplicate voltage readings
carBatteryData = carBatteryData[order(TimeStamp)]


# spliting all data, using the last date as testing data and the rest for training.
lastDate = max( as.Date( format( carBatteryData$TimeStamp, "%Y-%m-%d" ) ) )
trainingData = carBatteryData[ as.Date( format( carBatteryData$TimeStamp, "%Y-%m-%d" ) ) != lastDate ]
testingData = carBatteryData[ as.Date( format( carBatteryData$TimeStamp, "%Y-%m-%d" ) ) == lastDate ]



################################################################################
# Creating Anomaly Detection Model
################################################################################

h2o.init( nthreads = -1, max_mem_size = "5G" )
## 
## H2O is not running yet, starting it now...
##
## Note: In case of errors look at the following log files:
## C:\Users\LaranIkal\AppData\Local\Temp\Rtmp6lTw4H/h2o_LaranIkal_started_from_r.out
## C:\Users\LaranIkal\AppData\Local\Temp\Rtmp6lTw4H/h2o_LaranIkal_started_from_r.err
##
##
## Starting H2O JVM and connecting: Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 1 seconds 899 milliseconds
## H2O cluster timezone: America/Mexico_City
## H2O data parsing timezone: UTC
## H2O cluster version: 3.24.0.2
## H2O cluster version age: 1 month and 7 days
## H2O cluster name: H2O_started_from_R_LaranIkal_tzd452
## H2O cluster total nodes: 1
## H2O cluster total memory: 4.44 GB
## H2O cluster total cores: 8
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## H2O API Extensions: Amazon S3, Algos, AutoML, Core V3, Core V4
## R Version: R version 3.6.0 (2019-04-26)
  h2o.no_progress() # Disable progress bars for Rmd
h2o.removeAll() # Cleans h2o cluster state.
## [1] 0
  # Convert the training dataset to H2O format.
trainingData_hex = as.h2o( trainingData[,2], destination_frame = "train_hex" )

# Build an Isolation forest model
trainingModel = h2o.isolationForest( training_frame = trainingData_hex
, sample_rate = 0.1
, max_depth = 32
, ntrees = 100
)

# According to H2O doc:
# http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/if.html
#
# Isolation Forest is similar in principle to Random Forest and is built on the basis of decision trees.

# Isolation Forest creates multiple decision trees to isolate observations.
#
# Trees are split randomly, The assumption is that:
#
# IF ONE UNIT MEASUREMENTS ARE SIMILAR TO OTHERS,
# IT WILL TAKE MORE RANDOM SPLITS TO ISOLATE IT.
#
# The less splits needed, the unit is more likely to be anomalous.
#
# The average number of splits is then used as a score.

# Calculate score for training dataset
score <- h2o.predict( trainingModel, trainingData_hex )
result_pred <- as.vector( score$predict )


################################################################################
# Setting threshold value for anomaly detection.
################################################################################

# Setting desired threshold percentage.
threshold = .995 # Let's say we have 99.5% voltage values correct

# Using avobe threshold to get score limit to filter anomalous voltage readings.
scoreLimit = round( quantile( result_pred, threshold ), 4 )



################################################################################
# Get anomalous voltage readings from testing data, using model and scoreLimit got using training data.
################################################################################

# Convert testing data frame to H2O format.
testingDataH2O = as.h2o( testingData[,2], destination_frame = "testingData_hex" )

# Get score using training model
testingScore <- h2o.predict( trainingModel, testingDataH2O )

# Add row score at the beginning of testing dataset
testingData = cbind( RowScore = round( as.vector( testingScore$predict ), 4 ), testingData )

# Check if there are anomalous voltage readings from testing data
anomalies = testingData[ testingData$RowScore > scoreLimit, ]
# Here there is and additional filter to ensure maintenance recommendation
# If there are more than 3 anomalous voltage readings, display an alert.
if( dim( anomalies )[1] > 3 ) {
cat( "Show alert on car display: Battery got anomalous voltage readings, it is recommended to take it to service." )

plot_ly( data = anomalies
, x = ~TimeStamp
, y = ~BatteryVoltage
, type = 'scatter'
, mode = "lines"
, name = 'Anomalies') %>%
layout( yaxis = list( title = 'Battery Voltage.' )
, xaxis = list( categoryorder='trace', title = 'Date - Time.' )
)
}
## Show alert on car display: Battery got anomalous voltage readings, it is recommended to take it to service.

if( dim( anomalies )[1]  > 3 ) { 
DT::datatable(anomalies[,c(2,3)], rownames = FALSE )
}



Using this approach we may prevent failures on cars, not only for batteries but for many cases when sensors are used.

Carlos Kassab





We are using R, more information about R:


To leave a comment for the author, please follow the link and comment on their blog: R-Analytics.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)