This article describes the difference between commonly used terms, namely Digital Twin and IIoT Twin. A Digital Twin is a virtual software model that executes in a computer. An IIoT Twin is sensor based digital replication of an asset’s operational parameters visualized in the cloud, for purposes of measuring performance or health, or monitoring safety aspects.
Digital Twin
The term digital twin refers to a virtual computer simulation, software, which is created to help optimize the design of an asset such as a pump. Its goal is to have a digital replication of the asset, where you can do ‘what if’ analysis using the twin. In software, the developer provides various stimulus scenarios against a model, and then the developer would measure the responses to find the most optimal design parameters. The term Digital Twin really has nothing to do with IIoT other than many have mistakenly used IIoT and Digital Twin in the same sentence.
Technically, a digital twin starts out as an executable software model of a real-world asset or device. The model is created in a programming language like C/C++, and is driven by stimulus which is also programmed. Stimulus is organized by scenarios or tests to see the response of the model against various stimulus. The output of the computer simulation produces lots of data. This output may or may not correspond to the real-world device. This is because it is difficult to program all of the corner cases of assets. Imaging all of the corner cases associated to a pump, now image having to create a program which emulates those conditions. While this approach provides infinite testing possibilities, what if scenarios, and behaviors, the output is only as good as the fidelity and accuracy of the (executable) model. The primary use case for a digital twin is when you want to test numerous design options to see which is the most optimal, similar to finite element analysis.
IIoT Twin
This is the more appropriate term for most IIoT applications and is the term Chesterton uses to mean a sensed twin of a real-world asset, such as a pump represented in the Cloud. These real-world operational measurements come from industrial sensors mounted on the asset measuring key parameters such as pressure, power, humidity, vibration, temperature, etc. The accumulation of many sensors is the basis needed to create a digitally represented view of the actual pump. Sensor data is aggregated and organized to represent an asset, and when actual sensed data is compared against out of range values, the asset health can then be visualized.
The challenge for this approach is having enough sensors to properly represent all aspects of the asset. The most complex part of this, is the organization of data into an object orientation. The primary use case of an IIoT Twin is where you have many similar assets in the field you want to monitor. Sensors with digital twin object orientation is the enabler for increased IIoT driven optimizations, and a key part of the Industry 4.0 initiative. Here are the primary applications for IIoT Twin that are in use today;
- Health Monitor: The IIoT Twin can be configured to compare sensor data against out of range parameters to represent the health of the asset in real-time. This is the basis of condition monitoring.
- Performance Monitor: The IIoT Twin can be configured to compare the performance of the asset against original design specifications to see performance degradation over time.
- Safety Monitor: The IIoT Twin can provide warnings and alarms in real-time assets which have gone beyond their operational limits, potentially causing safety issues for personnel or equipment.
Want to proactively manage your pump reliability? Contact a Chesterton Specialist today to learn more about their innovative solutions. Learn more: https://chesterton.com/equipment-monitoring/chesterton-connect-iiot-home
