The era of digital transformation is upon us. With it comes a flood of maintenance-related information, surging in from various databases and other digital forms.
This information is highly valuable; it includes data about the condition of machines, tools, equipment, parts and many other assets, which can serve as a basis for constructing accurate digital models (i.e. “virtual representations”) of physical assets in the cyber world.
The models – called “Digital Twins” – are increasingly used to simulate and analyze industrial processes. Digital Twins can be seen as a natural extension of conventional models for the digital simulation of maintenance processes based on information derived from the physical world using sensors and Internet-of-Things (IoT) devices. Nevertheless, they can be also seen as a disruptor of digital simulation for enterprise maintenance based on their ability to fuse IoT information with events from digital simulation.
For an introduction on Digital Twins, watch this video:
Understanding the disruptive power of Digital Twins for maintenance applications begins with understanding their principal use cases in enterprise maintenance:
A Digital Twin provides the information needed to execute realistic and accurate simulations about the behavior of assets and their maintenance. Simulations based on Digital Twins take in information about risk factors, failure modes, operating scenarios and system configurations, in order to produce maintenance-related KPIs (Key Performance Indicators) such as:
Such simulations enable the anticipation of future maintenance activities, which is key for developing predictive maintenance systems. At the same time, they can improve the Planning of preventive and condition-based maintenance processes as a means of minimizing downtimes and unscheduled repairs.
Digital Twins are also used for what-if analysis of alternative maintenance scenarios. By simulating different maintenance scenarios, organizations can evaluate and select the most effective asset management strategy. What-if analysis can be exploited both for long-term planning of maintenance strategies (e.g., comparing a predictive maintenance strategy to a preventive one in terms of return on investment) and for short-term on-the-spot decision-making (e.g., whether it’s time to replace a tool or not).
In many cases, Digital Twins remain synchronized to the status of the physical assets they represent. Whenever the status of an asset is changed, the Digital Twin model updates to reflect the change. Likewise, whenever the status of the Digital Twin is changed as part of an IT operation, the respective change is reflected in the physical assets based on some IoT or Cyber-Physical System (CPS) that conveys the status of the digital world to the real world.
Based on this synchronization, Digital Twins can be used to configure the operation of assets and related physical systems. For example, if an IoT/CPS application detects a machine’s failure or degradation pattern, it could configure the machine to operate at a reduced speed. This can be done through an IT command to the Digital Twin of the machine, rather than a human operator of the machine.
It’s important to note that the use of Digital Twins for the flexible configuration of maintenance systems hinges on the deployment of proper CPS systems in the plant floor; notably, systems that can configure their physical parts based on information and commands from cyber counterparts.
In the medium- and long-term, we expect Digital Twins to drive open innovation in enterprise maintenance based on digital technologies. In particular, Digital Twins will be used as a vehicle for testing, validating and evaluating innovative ideas about when and how to maintain or repair an asset, without disrupting plant operations. This will facilitate innovators in their endeavors and will reduce the enterprise maintenance innovation cycles. In this context, IBM’s views Digital Twins as a way of transforming engines and other pieces of equipment to digital innovation platforms.
As you can see, these use cases provide clear benefits for plant operators and integrators of enterprise maintenance solutions. First, they give them the opportunity to gain insights on the production and asset management processes, such as non-obvious failure or degradation patterns for assets.
Based on such insights and knowledge, plant operators and solution integrators can better plan their maintenance strategies.
Second, Digital Twin simulations facilitate optimal decisions based on the evaluation of alternative maintenance scenarios. This leads to improved Overall Equipment Efficiency (OEE) and ultimately to a better Return on Investment (ROI) for the maintenance solutions.
Third, Digital Twins can be used to increase the automation and cost-effectiveness of the maintenance processes, through increased flexibility in the configuration of maintenance systems and the physical assets that they comprise.
Finally, Digital Twins can greatly facilitate the transition from traditional forms of maintenance (i.e. reactive maintenance and preventive maintenance) to emerging and more effective forms, such as predictive maintenance (PdM), with minimal disruption to shop floor operations.
Despite the ongoing digitalization of assets and maintenance processes, the construction and use of Digital Twins in maintenance applications remains a challenge.
The main issue concerns the design and construction of proper models for the assets and systems involved. It’s particularly complex to develop such models, as it requires an understanding of many different aspects of the maintenance processes, including:
It’s no question: it will be a struggle to get the best out of a Digital Twin deployment. For some, this argues well for a phased approach, which begins with simple models of assets and their failure modes, and gradually expands to more sophisticated ones. Such an approach can allow plant operators, equipment vendors, and solution integrators time to gain confidence in their Digital Twin deployments. It can also allow them to minimize relevant development and deployment risks.
In the wake of digital disruption, maintenance professionals everywhere are reflecting on their current enterprise maintenance systems and processes and analyzing the challenges – and possibilities – of embracing new innovations. Are you already collecting and leveraging digital information about your assets and field service processes? If yes, you can start considering the development of a Digital Twin model, along with related applications. This can help you take the right decisions at the right time, not to mention the potential to increase efficiency and yield sky-high ROI.
John Soldatos holds a Phd in Electrical & Computer Engineering. He is co-founder of the open source platform OpenIoT and has had a leading role in over 15 Internet-of-Things & BigData projects in manufacturing, logistics, smart energy, smart cities and healthcare. He has published more than 150 articles in international journals, books and conference proceedings, while he has authored numerous technical articles and blogs posts in the areas of IoT, cloud computing and BigData. He has recently edited and co-authored the book “Building Blocks for IoT Analytics”.