Today's edge devices offer analytics capabilities locally and in the cloud, providing actionable insights from key systems and production data
In the past, most PLCs were capable of controlling repetitive tasks in machines, but possessed the computing prowess of a smart toaster. Today’s Industrial PCs (IPCs) feature ample storage and powerhouse processors. Beckhoff PC-based controllers even offer Intel® Core™ i7, Intel® Xeon® and AMD Ryzen offerings, with four or as many as 36 cores. The automation software packages for these IPCs run alongside Windows, easily support third-party applications and can be accessed remotely. Most importantly, TwinCAT 3 automation software can provide advanced algorithms to manage data, such as pre-processing, compression, measurement and condition monitoring. This does not require a separate, stand-alone software platform.
Condition monitoring performs many operations locally, such as converting raw accelerometer data into the frequency domain. This can be done on an edge device or within the actual machine controllers’ PLC program. When analyzing vibration, for example, the information is often collected as a 0-10 volt or 4-20 mA signal. This can be changed to a more usable format on the controller through a Fast Fourier Transform (FFT) algorithm. More extensive evaluations of machine vibrations are possible using DIN ISO 10816-3. To monitor bearing life and other specific components, algorithms are readily available to add to a PLC program for calculating the envelope spectrum first and then the power spectrum. Many common machine conditions and predictive maintenance algorithms can be evaluated within the machine control, or on an edge device.
TwinCAT offers built-in algorithms to process both deterministic and stochastic data. If the data is deterministic, controllers using pre-processing algorithms could send certain values only upon a change, so the recipient should know the mathematic correlation and be able to reconstruct the original signal if desired. For stochastic data, the controller can send statistical information, such as the average value. Although the original signal is unknown, the recipient can still use compressed, statistical information.
It also is possible to implement algorithms on Beckhoff IPCs to monitor process data over a set sequence. This includes writing input data periodically, according to a configured number of learned points, to a file or to a database. After storing standard values, such as torque for a motion operation, algorithms compare cycle values against them. Ensuring the data are within a configured bandwidth creates a type of process window monitoring, which can readjust immediately since the local controller reacts in real-time.
Edge or cloud? Implementing both is best
Running advanced algorithms on a local edge device reduces cloud bandwidth requirements and offers an efficient solution for process optimization. However, that does not mean an operation can or should disconnect from the cloud. In the age of IIoT, it is essential to gather and easily access data across an operation, even if many analysis and decision-making tasks can be completed on local hardware first.
To decide what needs to be sent to the cloud and what can be processed or pre-processed locally, make sure to ask a few key questions. First, what are the goals your operation wants to achieve through data acquisition in this instance? Next, which data sets from which machines need to be analyzed in order to achieve these goals? Finally, what types of data insights does the operation need to improve efficiency and profitability?
Local monitoring with edge computing often works most efficiently to improve the operation of individual machines. However, the cloud provides the best platform to compare separate machines, production lines or manufacturing sites against each other. Implementing both allows an operation to maximize its capabilities.
Want to learn more about applying edge computing technologies to your machines and systems? Contact your local Beckhoff sales engineer today.
Daymon Thompson is the Automation Product Manager for Beckhoff Automation LLC.
A version of this article previously appeared in Control Engineering.
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