Data Aggregation

How to select and combine data from multiple sources to provide a consolidated chart.

Data Aggregation allows you to customise the data displayed in your charts for better insights and analysis.

• When creating charts, the data is always aggregated to some extent. This process helps condense large datasets into meaningful and manageable Visualisations.
• Decreasing the aggregation interval can enhance the precision of the data displayed in your chart. Smaller intervals allow you to capture more detailed information, providing a clearer picture of underlying trends and patterns.
• The default setting is 'Average,' which provides a balanced representation of the data. However, you have the flexibility to choose from various other functions based on your specific requirements.

Aggregation Details

The raw data is a series of measurements taken at a frequency appropriate for the monitored Asset. For example, motor speed could be sampled once every second (Interval = 1s), and this can be shown on a Visualisation for the last hour. If we wish to see trends over the whole day, it is too much data to display so we need to aggregate with bigger time intervals, for example one minute interval for a period of one day.

But how does RS Industria aggregate 60 measurements into 1 measurement? The default is an average of the measurements, but you can change this Function to suit your analysis.

The Functions are:

Average – Takes the arithmetic mean of all the measurements in the interval.

Maximum – Takes the highest measurement from within the interval.

Minimum – Takes the lowest measurement from within the interval.

Sum – Adds up all of the measurements in the interval.

Consumption – Shows the amount ‘consumed’ over the interval. Please note this will only work for Parameters implemented as meters, such as an electricity meter.

Standard Deviation – A statistical concept, roughly, the spread of the measurements within the interval. If all the measurements are the same, the Standard Deviation would be 0.

Availability % - The % uptime for an Asset over the interval. This will only work for Boolean status parameters that are 1 or 0.

Examples

• You are measuring vibration on a bearing, on a machine that has a variety of running speeds throughout the day. Plotting the Parameters ‘Vibration’ & ‘Machine Speed’, for a period of 6 months, ‘Maximum’ aggregation function with an ‘Daily’ Interval. Using the ‘Maximum’ function to analyse this data we will capture high start up vibrations but not necessarily normal running conditions, however this analysis could tell us about the machine health over a longer period. The Machine Speed will tell us if the machine didn’t run that day, or if it ran at a slower speed.
• Sticking with vibration, if a machine runs at one speed for a few days, and then changes to a different speed a different approach might be better. Looking at a period of 7 days, using the ‘Average’ function and ‘Hourly’ aggregation Interval. We can see if there are any fluctuations over a few hours/days and then set up a Rule or Visualisation with a Limit Line at a suitable baseline. Rules can accommodate logic statements to account for machine speed.