Digital Twins Roadmap: From Reactive to Prescriptive Maintenance

Industry Trends

The Industrial Internet of Things (IoT), the Digital Twins, and the connected devices continue to enable smart equipment and increase access to data.  While data collected by sensors have become priceless assets to the enterprise, the ability to make sense and use these data to drive new insight is where the real value lies.  With reactive maintenance, the asset runs until failure.  This maintenance approach is suited for non-critical assets that have little or no immediate impact on safety and have minimal repair or replacement costs.  In contrast, preventative maintenance strategies are intended to prevent asset failure.  These strategies suggest maintenance work to be conducted on a fixed schedule or based on operational statistics and manufacturer or industry recommendations.  Preventive maintenance can be managed in an enterprise asset management (EAM) or computerized maintenance management system (CMMS).

Predictive/Preventive/Prescriptive maintenance relies on the continuous monitoring of asset performance through sensor data and prediction engines to provide advanced warning of equipment problems and failures.  It typically uses advanced pattern recognition, machine learning or artificial intelligence (AI) and requires a predictive analytics solution for real-time insights into equipment health. 

Digital Twins as A Maintenance Strategy

A digital twin is a digital representation of a physical asset, like a pump, motor, turbine or even an entire industrial plant.  The digital twin allows operators to predict asset behaviors based on simulation of the asset in various conditions.

Digital Twins Digital%20Twins.JPG

The What’s and How’s for The Digital Twins

To effectively enable asset lifecycle management, the digital twin requires:

  • complete and continuous data input from asset design through operations
  • unified engineering in which process design, modeling and simulation are combined to create an integrated and collaborative workflow

The use of digital twin models allows:

  • analysis of processes, equipment and operations through multiple simulations for optimum safety, reliability and profitability
  • the digital clone of the asset to be updated in real time. 
  • users to optimize asset performance, reliability, and maintenance.
  • to continually update the maintenance workflow with ongoing operational and process data, such as maintenance and performance records
  • the predictive learning technology to proactively identify potential asset failures before they occur
  • sensor networks to become another data point in generating an asset's digital twin, particularly for legacy assets that were not born digital
  • a variety of cloud-based or on-premise tools to be applied to predict equipment failures before they occur while maintenance is scheduled around optimum economic and production conditions

Beyond maintaining physical asset reliability, maintenance strategies and solutions have broad impact across an entire enterprise.  As industrial organizations manage transitioning workforces, predictive analytics solutions can help ensure maintenance decisions and processes are captured and repeatable by incoming personnel.

ARC welcomes the opportunity to speak with technology suppliers and users alike about your activities, thoughts, perspectives and questions on this exciting area.  For further discussion or to provide feedback on this, please contact the author Jyoti Prakash at .


Keywords: Digital Twins, Predictive & Prescriptive Maintenance, IoT, Connected Devices, ARC Advisory Group.

Engage with ARC Advisory Group

Representative End User Clients
Representative Automation Clients
Representative Software Clients