A data historian has no problem collecting, archiving, and visualizing timestamped sensor data. As the cost of sensors decrease and technology improves, organizations will continue to gain more insight from their equipment through their sensors. Sensors for temperature, vibration, flow, voltage, RPM, and many more provide necessary data for engineers and operators to understand their historical and current equipment operation.
But what happens when you want to access data that’s not measured by a sensor? For example, there’s not an efficiency sensor for your plant. Or a performance sensor for a piece of equipment. This is where calculated tags (also known as script tags) come into play. Using mathematical, statistical, and logical equations, an organization can easily supplement their sensor data with calculated data to improve data analysis and the decision-making process.
Calculated Tags: Data Beyond Sensors
Sensors give you necessary information to understand how your equipment is running. They are also key to control and automation. But you don’t have to stop there. To improve on your sensor data, take advantage of their ability to serve as variables for additional insights. A simple example is the output of multiple units. There’s a sensor to measure the output individual units, but there’s not a sensor that measures the total across units. With a calculated tag, all you need to create is a mathematical equation adding all the outputs together. It’s that easy. Historians then save the new calculated tag into the database so you’re not having to constantly reinvent the wheel.
After creating a good name and description for your calculation tag, all members across the organization have access to more dynamic data for their analysis.
Software Tip: when evaluating your historian or a new historian, ask the software vendor about how they handle calculated tags. Most data historians base their pricing on the number of tags (or sensors) you monitor. What does this mean for calculated tags? Do calculated tags subtract from your tag number? In other words, do you have to pay for calculated tags?
To reduce costs and improve analysis, work with a data historian provider who doesn’t limit the number of calculated tags. With advancements made in server hardware and storage, you should be able to create as many calculated tags as needed.
Improved Visualizations and Context
Most data historians have Excel add-ins that organizations use for reporting and additional calculations. For example, what is the moving average for a specific tag or group of tags? With calculated tags though, users don’t have to go outside their historian to view this information. It’s as simple as selecting the tag and the time period to calculate the moving average.
By having these calculated tags within the system, organizations can easily pull up trends and additional visualizations. Rather have a moving average trend displayed on the historian dashboard than raw data, no problem. Create a calculated tag and you’re good to go.
Analysis Tip: transforming data into actionable intelligence is meant to improve decision-making. It should be a positive. But this requires the user to be aware of what they are looking at (e.g. moving average vs instantaneous read out) and be assured it’s quality data. If one piece of data is of bad quality, it will skew your calculated tag. As a result, make sure it’s easy to understand when a calculated tag is bad quality and should not be used. Your data historian’s built-in calculated tag tool (or as we call it a script editor) should also verify your calculations and provide any errors to fix prior to adding calculated tags to the database.
Easy Access and Consistency
Like your sensor data, your organization should not silo calculated tags to certain computers or people in the organization. Since a data historian solution adds calculated tags to the database once created, everyone in your organization who has access to your data historian should be able to use the calculated tags for analysis, visualization, and collaboration. This eliminates redundancy across the organization as multiple teams or people are not having to create their own variables within (or outside) of the data historian.
In addition, this improves consistency in data evaluation and analysis. For example, it’s easier to ensure you compare apples to apples when you add a calculated tag to the database. When you get into the more advanced calculated tags, there are different ways to measure and calculate. Once your organization decides its method to calculate for example efficiency, you’ll be sure that you are comparing the same calculation over different time periods or duration in reports.
Pro Tip: if you have multiple sites and teams, try to calculate similarly across the fleet. This may require additional time to get all sites onboard, but it will make your centralized team’s life easier and will simplify collaboration across sites. If each site creates calculated tags slightly different for a similar concept, it will be difficult in the long run to keep it all straight.
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