Title Image
Asset Twin

A Practical Approach for Digital Asset Twins

In today’s market, many buyers find the term “digital twin” confusing. What they need is a practical approach for building digital asset twins. So many vendors beef the concept up with 3D modeling, virtualization, augmented reality, and machine learning analytics or artificial intelligence.  But these references fall into the category of “too good to be true” or, more aptly described as “too early to be practical.” There is a time and place for artificial intelligence. Still, its cost and complexity generally limit the application to extremely high critical equipment.


There currently is no silver bullet technology, black box analytic, or artificial intelligence magic to solve your asset problems for the rest of the industrial world.  We have seen many organizations invest time, money, and resources pursuing the “magic” only to find narrow use cases. For example, anomaly detection can solve specific issues but does not provide practical and effective results to improve asset performance overall.


How Assets Can Fail

Improving asset performance involves understanding how an asset can fail and the proper countermeasures to guard against failure.  This scenario has typically been the focus of asset strategy development methods such as RCM or FMEA.  And while these methods are effective at defining “what” should be done or monitored, they typically fall short in terms of practical application and implementation.


“65% of respondents have not embraced strategy development methods like RCM due to lack of demonstrable results and credible ROI.”
Rethinking Asset Performance Management, ARC Advisory Group

A Practical Approach

We believe the low adoption rate is because most strategy development programs and solutions prove inadequate for actual implementation and operationalization.  Based on this reality and our experience, our company offers a more practical digital Asset Twin approach.  An Asset Twin is a generic model that an organization can deploy from any strategy development method. It focuses on operationalizing your asset strategy, both in terms of monitored conditions and activities performed.  It brings your asset strategy (or maintenance tactics) together seamlessly with asset health monitoring of conditions. In addition, the asset twin can track the work performed on the asset.


The Itus Digital Asset Twin Technology

Our Asset Twin actively monitors the asset and advises you of emerging failure risk and associated business consequences through three core constructs:


  • The Asset and its business objectives.  We start with the virtual definition of the Asset and provide a classification, mission time, and typical failure rate.  Armed with this knowledge, you can assess the overall failure risk in the context of operational consequences. Further, you can gauge the financial impact of the potential failure in terms of lost production, repairs, and other costs.


  • Dominant Failure Modes that point to the asset’s vulnerabilities. Our technology provides a definition of dominant Failure Mode definitions for the asset and presents it clearly and straightforwardly.  Also, for each Failure Mode, it gives the best approach to mitigate the failure risk.  By defining the conditions to monitor and best maintenance activities, you can protect the asset and extend its value and lifecycle.


  • Protections employed to guard against the failure modes occurring unexpectedly.  Protections are more than just a statement of intent to perform an activity or collect data; they are essentially the operationalization of the strategy.  Protections include active monitoring of data with analytics that trigger prescriptive and proactive action to mitigate emerging threats.  Two primary forms of protection can manage failure risk:  condition-based and activity-based.



An example of condition-based protection is monitoring a pressure drop across a filter. The platform issues a prescriptive action to replace the filter when the asset exceeds the pressure drop threshold.  A more complex example is monitoring excursion events and cumulative time outside of normal operating windows.  Further still, if the asset’s condition exceeds several events or an elapsed period outside the operating window, the protection triggers a remediating action to prevent an emerging threat.


In contrast, activity-based protections monitor the activities you have decided are necessary to maintain the asset.  For example, you may choose to replace a wear component upon some frequency, such as monthly.  An activity-based protection will monitor the occurrence of performing this work and compare against expected occurrence thresholds to the strategy.


Together, the (1) Asset, (2) Failure Modes, and (3) Protections provide an active digital model of current condition, risk, and prescriptive remediation as needed.  This Asset Twin provides the “operationalization” of the asset strategy to ensure business objectives are not interrupted.


The Digital Asset Twin provides a practical and effective approach. It virtually models your asset, improves equipment performance, and optimizes maintenance without the cost and complexity associated with a Digital Twin.  We based our approach on core reliability engineering principles that our platform applies to assets across all levels of criticality and consumable by industrial organizations of all sizes and maturity.