If you simply search the term, “digital twin”, you will see all kinds of references to 3D modeling, virtualization, augmented reality, machine learning analytics or artificial intelligence. All very interesting approaches to enhance asset management programs but sometimes hard to see how you can practically apply these technologies to quickly optimize your maintenance programs or equipment availability.
Currently there is no silver bullet technology, no black box analytic nor artificial intelligence magic to solve your asset problems. 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 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.
So, let’s take a step back and consider a more practical approach, the asset twin. From a practical industrial perspective, it is all about connecting to the right data to monitor failure risk and drive proactive action to avoid unplanned downtime events or other severe consequences. You will likely discover that you already have digital twins employed at your facilities. After this presentation, you will walk away with confidence to take your next steps, or first steps, on this digital journey.
“R.A.I.” the Reliability.aiTMChatbot
You can ask "R.A.I." anything about maintenance, reliability, and asset management.