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A Practical Approach to Digital Twins, the Asset Twin

If you simply search on the term “digital twin”, you will see all kinds of references to 3D modeling, virtualization, augmented reality, and references to machine learning analytics or artificial intelligence.  Very interesting visuals and links, but generally it all falls 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 Digital Twin use cases, but cost and complexity generally limit application to extremely high critical equipment, such as aircraft engines or with Original Equipment Manufacturers (OEMs) who seek to offer managed services or performance guarantees.  For the rest of the industrial world, there currently is no silver bullet technology, no black box analytic nor artificial intelligence magic to solve your asset problems.  We have seen a lot of folks invest a lot of time, money and resources pursuing the “magic” only to find very narrow use cases of anomaly detection that can solve for specific use cases but do not provide practical and effective results to improve asset performance overall.

Improving asset performance has always involved a proper understanding of how an asset can fail, and then having the proper countermeasures in place to guard against failure occurring.  This has typically been the focus of asset strategy development methods like RCM or FMEA.  While these methods are effective at defining the general “what” should be done or monitored, they typically fall very 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


We believe the low rate of adoption is because most strategy development programs and solutions fall very short on actual implementation and operationalization to achieve those desired results.  From this reality and our experience, we offer a more practical approach, which we call the Asset Twin.  An Asset Twin is a generic model that can be deployed from any strategy development method and focused on the operationalization of your strategy, both monitored conditions and activities performed.  It brings your asset strategy (or maintenance tactics) together seamlessly with asset health monitoring of conditions in addition to tracking the work performed on the asset.



An Asset Twin actively monitors the asset and advises 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.  With this understanding you can assess the overall risk of failure in terms operational consequence, ideally monetized to the full impact of a potential failure in terms of lost production, repairs, and other costs.


  • The dominant Failure Modes for which the asset is vulnerable.  We provide a definition of dominant Failure Modes for the Asset.  These are presented in a manner that is practical and not overwhelming.  For each Failure Mode, we also provide the best approach to mitigate the failure risk.  These are typically conditions to be monitored and activities to be done to maintain the asset operation and are the basis for protection.


  • The 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 analysis that triggers prescriptive and proactive action to mitigate emerging threats.  There are two primary forms of protections which can manage failure risk, condition-based and activity-based.


A condition-based protection, in its simplest terms, could be the monitoring of a pressure drop across a filter with a prescriptive action to replace the filter when a threshold is exceeded.  A more complex condition-based protection would monitor excursion events and cumulative time outside of normal operating windows.  Upon exceeding several events or elapsed time outside of the operating window, an action could be triggered to remediate an emerging failure threat.

An activity-based protection provides monitoring of the activities you have decided are necessary to maintain the asset.  For example, you may decide 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 Asset Twin provides a practical and effective approach to virtually model your asset, improve equipment performance and optimize maintenance without the cost and complexity associated with a Digital Twin.  The approach is based upon core reliability engineering principles, can be applied to assets across all levels of criticality and consumable by industrial organizations of all sizes and maturity.  Connect with us to learn more about Itus Digital and Asset Twins.




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