by Exosite, on May 16, 2017
The concept of a digital twin is a popular topic right now, but what is it? Long story short, it keeps the physical and virtual data from a device in one place. As an industrial IoT platform provider, we give birth to these digital siblings all the time.
But why are they useful? There are several ways to get information from a device:
Observing a Non-Connected Asset
Direct observation or measurement of an asset is often the most familiar, most accessible means of understanding its condition and state. Want to know the condition of a compressor? Chances are, walking past it could provide some sense of its condition through direct observation of its key properties, especially if there is unusual noise, vibration, or heat.
Instruments and gages can facilitate this direct observation by quantifying what can often be sensed through observation. Note, however, that concern is not typically given to the state of the measurement device or sensor, but rather for the asset it is measuring. This is an important distinction to make when beginning to think about connected solutions.
Local Monitoring (SCADA, M2M)
Early condition-monitoring solutions would typically map signals from sensors to channels or memory addresses, with physical displays or user interfaces that would directly represent these values. The need for direct observation of the asset was removed, but it was up to the observer to infer or correlate these measured values with asset state, condition, and maintenance records.
First-Generation IoT
Many examples of first-generation IoT solutions overlaid this local monitoring design pattern on top of Internet communication protocols and software user interfaces. Database schemas and user interfaces reflected the sensor and hardware values and layout, and similarly relied on user interpretation to correlate data and events, real-world conditions, and maintenance records, which were often stored in a separate, disconnected system.
It may seem logical to scale a first-generation IoT solution by adding sensor types and increasing the range of data captured; however, simply filling databases with streaming data from sensors leads to systems only data scientists are be able to use, as most people have trouble identifying with databases. However, if this information can be presented in a meaningful model, it is possible to enable users to forget about the data and instead react to meaningful knowledge.
This is the purpose of the digital twin—to transform sensor and device data into tangible asset knowledge for business users, application users, automated processes, and more.
If you’d like to learn more about the different aspects of the digital twin, click below. In case you’d like to get started on connecting your products and creating digital twins for yourself, sign up for a free trial of our enterprise IoT platform, Murano.
