Internet of Things, Industrial IoT, Artificial Intelligence, Big Data Analytics, and many more buzzwords have been dominating the current discussions on digital transformation. ‘Digital Twin’ a term encompassing the principles of all the above words is catching up in the digitalization talk.
Digital twin is the virtual model of a physical asset. Every component of the physical asset is represented in the virtual model along with its functionality. The virtual model is connected to the physical asset over a network to collect data from the operations and give inputs into the working of the physical asset. It has been introduced as a part of Product Lifecycle Management at the University of Michigan in 2003. The term has caught up in recent times due to an increasing focus on digitalization.
For instance, a wind turbine can be digitalized to create a digital twin. The digital twin will have every component represented such as blades, motors, and a gearbox, among others. Every parameter measured such as the speed of the blades, wind speed from anemometer, various motors controller values, and the output electricity is represented. The data is stored and real time analytics give inputs for optimal performance of the wind turbine. With the advancements in sensing technologies, every data point from the asset can be collected and analyzed in real time.
Old pickle in a New Jar or is it a new pickle!
The existing machines in the industry already work on a certain level of digital representation. Currently, the systems have a few goals such as monitoring, error or anomaly indication, mostly for sustenance and day-to-day working. The upcoming models are much more powerful: they can store data, analyze through running various analytical models, and compare data across machines at a central level. This enables analytical capability for prediction, optimized actions for maximizing the performance of the asset.
For example, a digital twin of a wind machine can diagnose defects based on the data such as abnormal vibrations. This can be used to prevent unscheduled downtime. Similarly, output can be predicted from historical data and predictive analytics, facilitating long term planning. The digital twin also enables equipment testing in a virtual environment.
GE: A step ahead of others
Among the industrial companies, GE has invested heavily in the IIoT segment as a part of their digital transformation endeavors. GE Digital has launched ‘Predix’ as an IIoT solution, bringing all industrial solutions together and offering them as a Cloud-based solution. Applications built on Predix connect industrial assets while collecting and analyzing data to deliver real time insights. This helps in optimizing industrial infrastructure and operations. This supports both GE and non-GE assets. The platform streamlines and speeds up the entire process of developing, deploying, operating, and monetizing Industrial Internet applications. Predix helps in creating Digital Twins for the physical assets. Predix is capable of handling big data i.e. it is able to handle the huge volume, velocity, and variety of machine data generated across the industries without compromising on security.
A promising future but a few roadblocks ahead
We are on the verge of a digital twin technology explosion as companies digitalize more and more assets, leading to an explosion of data. Data processing capabilities have to be improved. The complexity of the digital twin will increase as new sensing technologies come up. International Data Corporation predicts that, by 2018, companies who invest in digital twin technology will see a 30 percent improvement in cycle times of critical processes. Now industrial companies such as GE can also offer services instead of selling the equipment. Output-based services will become the new norm.
IoT has more to offer in the Industrial applications segment than in the consumer domain. Estimated savings of 50% in fixed assets is achievable if all physical assets are digitalized and everything is connected.
With data generating in huge volumes, storage, retrieval, analytics, interoperability, and security have become critical components. Data management systems have to translate and support various protocols and data formats. Access controls and several new protocols have to be created. More analytical models need to be developed. Standardization has to be done in terms of data flow and protocols used. Artificial Intelligence also has its role in the transformation. Similar is the importance of big data analytics. Digital Twin technology is not a single technology but the amalgamation of all digital technologies. Companies have to invest in the technologies in tandem to reap the benefits of digitalization.
References
- Digital Twin: Manufacturing Excellence through Virtual Factory Replication, whitepaper by Dr. Michael Grieves
- http://www.forbes.com/sites/default/files/images/inline-migration/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-so-important/2/#58ac259b3227; accessed on 24/06/2017
- www.ge.com/digital/industrial-internet/digital-twin; accessed on 24/06/2017
- The Future for Industrial Services: The Digital Twin; source: www.infosys.com/insights/services-being-digital/Documents/future-industrial-digital.pdf
- www.capgemini.com/blog/capping-it-off/2017/05/tolkeins-re-imagined-two-towers-digital-twins; accessed on 25/06/2017
- www.ibm.com/blogs/internet-of-things/digital-twin; accessed on 25/06/2017
