Digital twin manufacturing tools are enabling manufacturing companies to stay ahead of problems, predict downtimes, equipment failures, and simulate solutions before implementing them with failsafe methods.
Large enterprises and manufacturing companies like GE in the healthcare and industrial sectors are utilizing digital twin technology. Unilever in the consumer goods sector and BMW in the automotive manufacturing industry are also using digital twin technology.
It’s already revolutionizing predictive maintenance solutions by providing virtual replicas of physical manufacturing systems. Furthermore, simulations of large and intricate manufacturing systems are great for fault detection and proactive maintenance planning. Implementation of digital twin technologies, ultimately, leads to reduced downtime and optimized resource use.
5 Ways Digital Twin Technology is Revolutionizing Predictive Maintenance
We have found five different ways digital simulation tools and digital twin systems are revolutionizing the manufacturing landscape and predictive maintenance.
Here’s how digital twin for manufacturing is supercharging predictive maintenance:
1. Real-time Monitoring and Simulation
Real-time monitoring and simulation is a bliss in the field of predictive maintenance. With the use of digital twin technology, manufacturing and other machinery can create a replica of an existing physical asset.
These assets have sensors embedded or attached to them. The sensor is capable of transmitting different data, such as temperature, pressure, and vibration, to the digital replica of the asset.
Therefore, these industrial IoT sensors allow virtual models to mirror the behaviors of the physical asset through real-time data tracking. The digital twin system uses both historical and real-time data to ensure predictive maintenance. The real-time monitoring of data and the use of simulations ensure that there are always solutions ready before the problem arises.
2. Predictive Maintenance
As discussed, predictive maintenance software can use the power of AI and data sourced from different touchpoints of the physical system. It goes to the digital twin system. With this process, it’s easier for the maintenance team to recognize when and where the system is failing or likely to fail.
Manufacturing companies can use this process to proactively monitor the activities of the operating system. This predictive measure helps them decide which part to replace and when.
A digital twin system of the entire manufacturing equipment is a way to make the operation fail-safe. This helps maintenance teams predict, reduce repair costs, and optimize resource allocation.
3. Enhanced Decision-Making
Digital twin technology is indeed great for anticipating manufacturing problems before they happen. However, that’s the surface-level feature most manufacturing companies are using.
Some platforms are taking it to the next level by using digital twin systems for scenario planning. It’s a process to plan scenarios and manufacturing processes to simulate failures and successes.
Manufacturing companies can simulate different strategies and implement them virtually through digital twin systems. The simulation, in alliance with historical and real-time data, allows manufacturers to test their strategies before deploying them in practice. Digital twin technology enhances decision-making, allowing for better resource allocation and parts management for different manufacturing equipment.
4. Remote Diagnostics and Troubleshooting
Digital twin technology simulation of real and physical assets allows manufacturing companies to identify issues and troubleshoot in isolation. Experts can analyze the equipment remotely without having to interfere with the entire manufacturing system.
Remote diagnosis of manufacturing equipment and systems takes place in two different phases. First, they go through the physical system and collect necessary information for integration.
The next process is all about building a virtual modelling and simulation of the system for potential problem detection.
A. Data Collection and Integration
Manufacturers have to use sensors installed in the physical devices to collect necessary information. These data include vibration, energy consumption, and the temperature of the manufacturing equipment. The data is streamed and transmitted to the digital twin technology in real time.
Finally, the digital twin system uses both real-time and historical data to represent the functionality of the manufacturing system. This process facilitates a digital simulation of the asset and its performance in real time.
B. Testing through Simulations
The next step is all about creating a virtual model of the real and physical asset through virtual modelling techniques. Both real-time and historical data contribute to the formation of this new model.
This process helps manufacturers digitally replicate the structure, function, and performance of the asset. Engineers can use this simulated replica to understand how the system will perform or react under certain conditions. In fact, sometimes they use the simulation to build different strategies and check the efficacy of their plan.
This type of remote diagnostic feature, through digital twins, helps different sectors. In aerospace, it helps with equipment failure detection and optimizing manufacturing operations.
A good example would be Rolls-Royce. They use digital twins for design improvements. Furthermore, both manufacturing and healthcare industries benefit from the problem-solving capabilities that the digital simulation process allows.
5. Cost Savings and Efficiency Gains
Digital twins help reduce downtime, improve asset lifespan, and optimize maintenance costs. Manufacturing companies often have to check their operations and equipment efficiency.
Instead of disrupting the operation through frequent inspection, engineers can rely on the digital twin technology. They can create virtual models of physical equipment. Therefore, it’s easier to inspect and run tests on the equipment and systems in isolation.
Such an easy inspection process makes asset performance management easier, more refined, and cost-efficient. Here’s how digital twinning of manufacturing equipment helps achieve cost efficiency:
- Digital twinning helps reduce manufacturing downtime, improve asset lifespan, and help optimize manufacturing costs.
- It prevents unexpected breakdowns of manufacturing equipment, thereby increasing equipment lifespan through constant maintenance.
- Optimized resource utilization and maintenance scheduling help reduce maintenance-related expenses.
Digital twins are not only helping simulate physical manufacturing processes in a digital environment. But also, it’s a process that helps improve manufacturing cost, reduce downtime, and use a data-driven and proactive measure to stay ahead of competitive manufacturing businesses.
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The Future of Predictive Maintenance
The future of predictive maintenance will include various digital twin technology and the integration of AI maintenance. The use of AI and machine learning is already revolutionizing data collection and analysis.
With digital twin technology and AI integration, predictive maintenance for manufacturing is seeing a new horizon. Manufacturing companies will now be ten times faster than they used to be.
The benefits of new technology integration will reflect on operations, cost efficiency, and resource allocation for equipment management and maintenance. Are you considering the use of a digital twin for your manufacturing company? Which part of its functionalities do you think will improve your business? The comment box is all yours to share an opinion.
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