Optimizing High-Speed Data Capture in PLCs: Queues, UDTs, and Smart Polling Strategies

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In this clip, we build on earlier concepts of queue-based data handling in PLCs by exploring how to manage more complex datasets using User Defined Types (UDTs) and timestamped arrays. This approach allows control engineers to store, organize, and analyze time-series data efficiently inside the PLC without overwhelming supervisory systems like SCADA or MES.

Using a servo drive as an example, we demonstrate how UDTs can include various data types such as integers, real values, and arrays. You might store values like speed, voltage, torque, and gear ratio all within one UDT. Then, by creating an array of these UDTs, you form a software queue that tracks how this information evolves across each PLC scan cycle.

Here’s why this matters.

If your PLC scans every 5 milliseconds and you store 200 entries in your queue, you can capture one second of historical data at that resolution. This becomes useful in high-speed or high-precision applications where knowing the state of the machine across time is essential for diagnostics, quality assurance, or advanced analytics.

We also explain how this model allows you to separate data acquisition timing from data consumption. For example, you can collect data at the millisecond level inside your PLC, but only pull and process it every few seconds or minutes from the SCADA system or cloud. This reduces bandwidth usage while maintaining visibility into the behavior of your system.

Key insights from this video include:

✅ How to create UDTs for organizing complex equipment data
✅ Why arrays of UDTs act like time-based queues in software
✅ Using timestamped registers to track changes over time
✅ Filtering and downsampling strategies for SCADA, MES, or cloud-based platforms
✅ When high-frequency polling is unnecessary or even wasteful
✅ Common use cases including greenfield design, system optimization, and bottleneck analysis

This technique is not for every application. It is best suited for high-speed operations or advanced troubleshooting efforts where standard polling intervals fall short. For plants aiming to squeeze out the last few percentage points of efficiency or ensure their control strategy scales with future needs, this approach offers clarity and flexibility.

If you work in controls, automation, or plant engineering and want to move beyond the basics of PLC logic, understanding how to structure and handle time-series data like this is essential.

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