Digital twins and predictive maintenance: the winning combo?

Digital twins and predictive maintenance: the winning combo?

In 2002, during his speech at the University of Michigan, Dr. Michael Grieves unveiled for the first time his theory of “Digital Twins”. Behind this rather obscure term lies the idea of developing a digital copy of a physical system based on its own data. It will take more than ten years and the advent of Industry 4.0 for organizations to have the necessary technological resources to achieve this vision.

Aerospace and aeronautics sectors, as well as homeland security were the first actors on the market to explore its potential as part of the drawing up of their production and development strategies. Today, the concept of digital twin is on thriving and appeals to all industry players, as it benefits from improved data storage and analysis capacities, IoT (Internet of Things) and artificial intelligence. Digital twins appear to be the next step towards manufacturers’ digital transformation.

What are digital twins ( DT )?

By DT, we understand the digital reproduction of a physical system’s information. In other words, a digital twin is the exact replica - in digital format - of a process, a product or a service, designed with a modeling software or a CAD tool (computer-aided design).

According to Michael Grieves, the existence of a DT relies on three factors:

  1. The existence of a physical asset in its environment (for example a functioning reamer).
  2. The development of a copy in a digital environment.
  3. The exchange of information linking the two environments, thanks to innovative solutions of Industry 4.0 such as smart sensors.

Even though it does not replace its physical model, a digital twin integrates all its features. Therefore, development, production and industrial maintenance teams leverage this visual representation and the data it processes for simulation, monitoring, maintenance and optimization purposes.

In this regard, DT show real benefits as they are highly flexible and can adapt to any scale of their physical system. Therefore, it is possible to develop a digital twin of a production line, a piece of equipment or a sub-equipment, without any dimension constraint. In the long term, the more companies deploy DT on-site, the greater visibility over their machines fleet they will get in real-time, allowing them to implement a strategy of continuous improvement.

Choosing for a strategy of continuous improvement

By encouraging organizations to commit to an approach based on the continuous improvement of their equipment, the concept of digital twins itself is supporting innovation in the industrial sector.

With the rise of Industry 4.0, data-driven decisions have become the norm. Then, thanks to the introduction of the Industrial Internet of Things (IIoT), all departments of the same company are now interconnected, information is exchanged quickly and seamlessly along the chain of value. Spare parts, for instance, are no longer purchased according to a defined schedule but to the real needs that maintenance professionals communicate to procurement teams through a next-gen CMMS like Mobility Work.

How do DT work?

The development of a digital twin is deeply linked to the use of smart sensors along the production chain. These sensors collect a great amount of data on the operational state and the environment of a piece of equipment (the physical twin), which are then sent to the digital twin. All this data is then aggregated and completed by the organization’s data: company’s systems, design requirements, equipment and sub-equipment nomenclatures, to name a few.

At Mobility Work, data analytics is a priority. This is why our next-gen CMMS features a powerful analytics tools, offering an alternative to traditional and - often - time-consuming methods (Excel files, SQL queries, etc.). We developed a tool for the industrial sector that enables maintenance managers to easily analyze their maintenance data and to make the right decision.



The analytics tool of Mobility Work next-gen CMMS allows you to monitor the evolution of your KPIs in real time

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In a second time, all the data is analyzed through algorithmic simulations and visualization routines integrated into the digital twin. The information collected by smart sensors are reproduced onto the visual environment: the effective state of a piece of equipment is depicted in real time by its virtual replica. Maintenance experts then have efficient means to identify potential deviations from optimum production conditions. Lastly, these observations are used to improve maintenance routines.

DT, a lever for innovation?

From production to procurement and R&D, every level of the organization capitalizes on the latest technology innovations, in order to tailor their production to the market’s expectations quickly and at the lowest cost.

Traditionally, products and equipment were manufactured and then optimized through a series of standardized tests. This generally led to high production costs and times for the organizations. Thanks to DT, it is now possible to design, test and improve a product in a virtual environment, before even launching its production.

Moreover, the use of DT also enables maintenance professionals to have a better understanding of the life cycle of their products and equipment. They can adjust their strategy of industrial maintenance and deploy routines of predictive maintenance.

Improve your strategy of predictive maintenance

By creating a digital copy of a piece of equipment or a production chain, you can simplify the access to information as well as the planning of industrial maintenance interventions. Maintenance experts can forecast downtimes and automate their operations of predictive maintenance thanks to Mobility Work, their next-gen CMMS.

As they combine data from digital simulations and IoT, maintenance professionals are able to detect any gap and prevent potential equipment anomaly or failure. In this regard, innovative solutions such as Mobility Work, the first mobile and community-based CMMS platform, allow technicians to link sensors directly to their CMMS.

Say, for instance, that the oil level of an engine reaches a critical level. The information is immediately sent through the smart sensors installed on the machine to the digital twin and transcribed onto the technical teams’ device. Maintenance teams can immediately plan an intervention from their maintenance management software (CMMS).



Thanks to Mobility Work CMMS, you can easily create and access your maintenance planning

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The ability to simulate maintenance interventions has opened new horizons to maintenance teams: field technicians actively participate in the deployment of a predictive maintenance strategy as they forecast downtimes and adapt their maintenance routines to the actual life cycle of their equipment; as for maintenance managers, they can test different predictive maintenance scenarios, sit back and reflect on their strategy and identify areas of improvement.

According to a study published by Gartner and IDC (International Data Corporation), by 2020 almost 50% of major manufacturing players will have or be about to integrate DT technologies into their process. Although this may represent a substantial initial investment, the deployment of these digital twins opens up new opportunities for the industry to improve your predictive maintenance strategy. This way, in the long term, you can eliminate unexpected downtime, reduce maintenance costs, optimize equipment reliability and extend equipment life.

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