Industrial IoT has opened many windows including data monitoring of devices and physical assets- which has, in turn, helped derive predictive insights. With monitored data, are now able to create Digital Twins of systems.
What is a Digital Twin?
Digital Twin is a real-time image of assets or systems that help optimize business performance. IoT has made the digital twin more accessible and cost efficient for industries. It incorporates Big Data, Artificial Intelligence (AI), Machine Learning (ML) and IoT to create a mechanism to visualize assets.
Attempting to make improvements in your real-world assets each time would prove to be highly expensive. For instance, trying to increase power supply of a Motor above the ideal range to check how the new winding will react, is a risky proposition as it may involve the motor breaking down. This will result in the wastage of a lot of resources. What if this test could first be made on a digital twin of the motor? Would it not be easier and more viable to detect faults or even encounter a breakdown on a virtual motor? Digital Twins will enable us to test decisions we would like to make on real physical assets.
Adoption and Usage of Digital Twin
Adoption of Digital Twin may sometimes take time, as the readiness of internal system towards implementing Digital Twin may differ in each case. It is, however, a commonly agreed point that adoption will ultimately benefit organizations greatly and must be done in the closest timelines possible in order to not be left behind in the wave, if not anything else. The key to adoption is in starting one step at a time- asset by asset and department by department. Implementation of Digital Twin will be challenging and interesting as it will include data of machines, ongoing work details, system details, process details getting continuously updated in the digital twin application. It will ultimately represent the digital side of the physical world.
The aim of the digital twin application is to find abnormalities for improving quality and efficiency. The outcome of this will decide the set of actions to be taken in the real world. With emerging technology of connecting devices, data monitoring, cloud computing, edge computing making it cost-effective, organization prefer this route as it saves them immense time, money (breakdown and maintenance costs), manpower and also is a more energy efficient practice. The best approach for implementation is to set goals, identify areas or processes, run a pilot, and then scale up slowly.
Use cases of Digital Twin
For instance, a steel manufacturing plant produces a billion dollars’ worth of steel products annually. From upstream to downstream production, there are continuous production lines with specific configuration to produce a particular grade of steel. The team needs to continuously monitor machines / raw material/output to have quality production by reducing unplanned downtime, reducing scrap and error in machine configurations. Usually, the team analyzes issues in production by mining data to identify information that can help. They then start connecting the dots to identify the exact root cause of the issue.
With Digital Twin: The team now gets data from machines and IT systems to show as real-time copy of production line. They can read data from every machine input and output with the operator’s inputs. Digital Twin will show how and where the issue has occurred, helping the team to identify the issue while also suggesting actions to be taken to avoid the same issue in the future.
Ecolibrium uses its technology- SmartSense- in over 500 organizations to create digital twins for large industries and commercial buildings- and uses it to perform predictive maintenance.
Written by Swapnil Pensalwar, Product Delivery Manager at Ecolibrium
Swapnil has been involved in several IoT and Data Science driven product development assignments. Swapnil has over 10 Years of experience, playing different roles such as Java developer, Business Analyst, Planning and Execution expert, Integration Expert and Product Manager over these years.