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Digital Twins Bring Value to Big RFID and IoT Data

PACCAR is using a digital twin model to manage the maintenance and repair of some engines, by creating a virtual version of an engine based on sensor data from the real-world versions.
By Claire Swedberg
Apr 11, 2018

Radio frequency identification (RFID)- and Internet of Things (IoT)-based systems (wireless or wired) have been collecting location and sensor data for years. However, an ongoing challenge has been how to manage the growing volume of accumulated data. In some cases, the solutions to manage that data have lagged far behind the hardware capabilities to collect it.

One way in which companies are now making use of the data from RFID, real-time location system (RTLS) and IoT solutions is to create replicas of real assets—known as digital twins—so that they can be measured against them, or put to the test, virtually. The twin concept has resulted from the wealth of data generated by the wireless transmission of sensor and location data regarding things and people.

Bsquare's David McCarthy
By leveraging a digital representation of a physical asset, users can gain intelligence that can help them to test behavioral responses to conditions, or to predict failures. It's the next step, some analysts say, to understanding and managing the vast volumes of data that come from sensors via RFID transmissions or other forms of wireless communication.

Typically, digital twin technology is most recently being employed for better predictive maintenance and repair, though it could cross multiple industries and market sectors, says David McCarthy, the senior director at IoT software company Bsquare, which offers a solution to customers in the industrial sector known as DataV. Bsquare has focused for several decades on bringing intelligence to physical assets, first as machine-to-machine data. Throughout the past year, the firm's customers have used its DataV software stack to predict failures and capture data-driven diagnostics.

A digital twin can be used to set up baseline performance expectations and real-time comparisons against other devices, McCarthy explains. By creating this virtual device, based on sensor data from the real things, users can better understand how their equipment should be performing—and how it actually is performing. This enables the users to accurately predict when maintenance may be necessary, when a failure is imminent and what conditions are most favorable for a device's operation.

PACCAR is using Bsquare's solution to create digital versions of its equipment in order to create repair scenarios. The company can create a master twin against which real engines or parts can be compared. The data is being collected from sensors applied to engines. As information is received in the DataV system, it is correlated and compared against the conditions under which the engine may operate. A typical engine, under specific conditions, can then be created in the software.

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