Tag: R

Build Machine Components Timeline Using R Visual in Power BI

One common approach to detect exceptions of a machine is to monitor the correlative status of components in the machine. For example, in normal condition, two or more components should be running at the same time, or some components should be running in sequential order. When the components are not running in the way as expected, that indicates potential issues with the machine which need attentions from engineers.

Thanks to IoT sensors, it is easy to capture the data of component status in a machine. To help engineers to easily pick up the potential issues, a machine components timeline chart will be very helpful. However, Power BI does not provide this kind of timeline chart, and it can be time consuming to build custom Javascript-based visuals. Fortunately, we have R visual in Power BI. With a little help from ggplot2 library, we can easily build a timeline chart (as the one shown below) in four lines code.

f1

Firstly, we need add a R visual onto Power BI canvas, and set the data fields required for the chart.

f2

The minimal fields required are the name/code of the component, start date and end date of the component running cycle, such as:

ComponentCode Start DateTime End DateTime
Pump 2017-01-01 09:12:35 2017-01-01 09:18:37
Motor 2017-01-01 09:12:35 2017-01-01 09:18:37

In the R script editor, we use the geom_segment to draw each component running cycles. The x axis is for the time of component running, and the y axis is for the component name/code.

f3

Apart from showing a Power BI timeline chart for engineers to detect the machine issue as this blog post introduced, we can also send alerts to engineers using Azure Functions.

Advertisements

IoT Machine Learning 3 – Extracting Features from IoT Sensor Data using R

In my previous blog I introduced the common patterns to extract features from IoT sensor data using Python. Although R is not my primary machine learning language it is becoming ubiquitous in Microsoft’s data analytics ecosystem after they acquired Revolution Analytics, the major commercial distributor of R. Considering the increasing popularity of R on Microsoft data platforms, I will create the R version of code for IoT data feature extraction in this blog.

This blog post is also organised based on the three common patterns for extracting feature from IoT sensor data:

  • Window-based descriptive statistics
  • Seasonal pattern
  • Trend pattern

Also, the examples use the same IoT sample data that stores the hourly reading from sensor A.

a1

  1. Window-based descriptive statistics

We can use the rollapply function in the zoo library to calculate the descriptive statistics values in a rolling window. As there is no function for Skewness in the core R packages we have to use the e1071 library that contains the Skewness and Kurtosis function.

data <- data %>%
        mutate(SensorA_Mean_12h=rollapply(SensorA, width=12, FUN=mean, by=1, fill=NA, align='right'),
               SensorA_SD_12h=rollapply(SensorA, width=12, FUN=sd, by=1, fill=NA, align='right'),
               SensorA_Skew_12h=rollapply(SensorA, width=12, FUN=skewness, by=1, fill=NA, align='right'),
               SensorA_Mean_24h=rollapply(SensorA, width=24, FUN=mean, by=1, fill=NA, align='right'),
               SensorA_SD_24h=rollapply(SensorA, width=24, FUN=sd, by=1, fill=NA, align='right'),
               SensorA_Skew_24h=rollapply(SensorA, width=24, FUN=skewness, by=1, fill=NA, align='right'),
               SensorA_Mean_72h=rollapply(SensorA, width=72, FUN=mean, by=1, fill=NA, align='right'),
               SensorA_SD_72h=rollapply(SensorA, width=72, FUN=sd, by=1, fill=NA, align='right'),
               SensorA_Skew_72h=rollapply(SensorA, width=72, FUN=skewness, by=1, fill=NA, align='right')
               ) 
tail(data, 5)

The code above will generate the following features:

a1

  1. Seasonal pattern

A date + time is represented in R as an object of class POSIXct. Once we convert the DateTime column into POSIXct, we can easily extract the parts of the datatime.

data$Date <- as.POSIXct(data$Date, "%Y-%m-%dT%H:%M:%S", tz="UTC")

data$DayOfWeek <- as.numeric(format(data$Date, "%u"))
data$IsWeekend <- ifelse (data$DayOfWeek>5, 1, 0)
data$Hour <- as.numeric(format(data$Date, "%H"))
data$IsWorkingHour <- ifelse (data$Hour>=9 & data$Hour<=17, 1, 0)
data$Year <- as.numeric(format(data$Date, "%Y"))
data$Month <- as.numeric(format(data$Date, "%m"))
data$DayOfMonth <- as.numeric(format(data$Date, "%d"))
tail(data, 5)

a2

  1. Trend pattern

In Python, we can use shift function to extract the features for representing the trend pattern in a time-series dataset. In R, a similar function is slide provided by DataCombine library.

data <- slide(data, Var = "SensorA", slideBy = -1:-7, 
      NewVar=c('SensorA_lag_1h', 'SensorA_lag_2h', 'SensorA_lag_3h', 'SensorA_lag_4h',
               'SensorA_lag_5h', 'SensorA_lag_6h', 'SensorA_lag_7h')
               )                                 
tail(data, 5)

We can the output as:
a3