At its simplest, the digital twin (DT) is a dynamic digital representation of a physical piece of equipment, or thing, together with its associated environment. Every DT has a dynamic data model containing a number of data attributes of the physical thing or system it represents. Attributes are associated with sensors that measure temperature, pressure, and other variables and associated physics in order to represent real world operating conditions as well as static values like the installation date or original equipment manufacturer (OEM).
A DT can also consist of multiple nested twins that provide narrower or wider views across equipment and assets based on the process or use case. For example, a complex asset like an oil refinery can have a DT for a compressor motor, the compressor, the process train served by the compressor, and for the entire multi-train plant. Depending on its size, the refinery could have anywhere from 50,000 to 500,000 sensors taking measurements represented in the DT. At the end of the day, digital twins provide the necessary schemas required to easily compare and benchmark like things against one another—helping the user/operator to understand what’s operating well and what’s not.
Three types of digital twins
As the size and complexity of DTs vary, so do functions and lifecycles. We like to think of three types:
• Status twins originate from the earliest design stages of the product cycle, mostly representing consumer products like a connected home or connected car. Data from product lifecycle management (PLM) systems is a major input, and use cases are typically device management, product control, and product quality. Most product twins have short service lives when compared to industrial assets.
• Operational twins enable industrial organizations to improve the operations of their complex plant and equipment and are used to support the work of engineers (process, reliability, etc.) and data scientists doing analytics and lifecycle operations. Operational DTs may inherit data from a status DT. Operational DTs may also include machine learning analytical models. Dan Miklovic at LNS Research calls these “Smart-Connected Asset” DTs. Operational DTs have long lifecycles, and will change over time.
• Simulation twins replicate equipment/device behavior and contain built-in physics models and even process models for what’s connected to the equipment. Simulation-twin use cases include simulations of how equipment performs under varying conditions, training and virtual reality (VR).
Other twins (cognitive twin or autonomous twin) are beginning to receive attention, and tend to be an amalgamation of those listed above
Use cases for the operational twin
Operations teams are looking for ways to improve asset utilization, cut operations and maintenance costs, optimize capital spend and reduce health, safety and environmental incidents. At the heart of every company’s digital transformation is a desire to achieve these objectives through analytical solutions that can augment, and even be proxies for engineers and technicians. But getting there is really hard, whether it’s working to deploy basic analytics like business intelligence, sophisticated machine-learning-driven analytics, or Industrial Internet of Things (IIoT) applications through platforms provided by OEM vendors like GE, Honeywell and ABB.
The biggest challenge is sensor data, which is mostly locked up in process historian systems and stored in a format (typically flat with no context) making analytics next to impossible. This data must be modeled and then kept continuously up to date to reflect the underlying state of affairs with the equipment and its associated asset. An operational DT solves this challenge by enabling the federation of data via the DT’s data model, which is built using metadata associated with the physical equipment.
Once this data model exists, any operations data represented by its metadata in the DT can be shipped to a data lake in an organized way for analytics. The DT’s data model can also be published at the edge to enable shipping data from one system to another, for example from a PI historian to an IIoT application like GE Predix APM. The Operational digital twin also allows for continuous maintenance of the operations data to reflect constantly changing real world conditions.
Five guiding principles
When it comes to building operational DTs that are both effective and sustainable, we have five ideas.
1. Leave no data behind. Data is the foundation for the DT, so bring it all together from the following sources:
• Time series data from data historians, IoT hubs/gateways, and telematics systems;
• Transactional data residing in Enterprise Asset Management, Laboratory Information Management System, Field Service Management System etc.; and
• Static data from spreadsheets, especially those left behind by engineering firms who built the plants, and process hazard data.
Federating as much data as possible via the DT will improve the value of IIoT analytics and applications, and also reveal new and valuable information about the physical twin that was previously unavailable. For example, a DT containing maintenance, equipment, sensor and process hazard data on a critical process can give operators brand new insights on the state of maintenance on critical equipment and how it relates to high risk process safety hazards.
2. Standardize equipment templates across the enterprise. The starting point for building operational DTs is theequipment template which allows for modeling theequipment, its sub-components, associated sensors,sensor attributes, and other related metadata like equipmentfunctional location. Asset operators too often relyon the OEM model, or try to keep the model limitedto only the data streams they presume they need.