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Digital Twin Software: Efficient machine monitoring & maintenance

Digital twins are regarded as the key concept for Industry 4.0.



As virtual images of objects or systems, digital twins are continuously updated with real-time data and enable companies to carry out dynamic analyses, simulations and optimizations. But what exactly is behind this technology? And how can the implementation of so-called “digital twin software” provide decisive advantages, especially in machine maintenance, in order to remain competitive?


 

Content:

Definition: What is a digital twin?


A digital twin is a virtual image of a physical object, process or system that is continuously updated and provides precise data on its current status.


In industry, for example, the digital twin is often used to analyze the behavior of products in different scenarios and environments and to map the entire life cycle of the object based on real-time data. In addition, simulations, machine learning and data-driven decision-making processes enable valuable conclusions to be drawn in order to make decisions.


Benefits and application of digital twins


With the digital twin, companies can simulate and optimize the behavior of systems under varying use cases and scenarios.

 

Digital twins are used in a wide variety of areas, such as production, manufacturing, healthcare and machine maintenance.

In industry, for example, they are used specifically to develop partial models for different system aspects, such as mechanical or electrical components.


Digital Twin Software in machine monitoring & maintenance


Now it's time to move from theory to practice: how do we at aiomatic use digital twins to create AI-based health predictions for optimised machine maintenance?


aiomatic's Digital Twin Software collects real-time data from machine sensors to create a virtual copy of the machine. By visualising and analysing the data, models can be developed that later provide maintenance teams with valuable insights into the current health of their machines. The behaviour of the machines in different environments and use cases naturally plays a crucial role in this process.




In the first step, the system collects relevant sensor data from a machine to be monitored over an extended period of time - for example, the engine vibration and oil temperature of a pump's gearbox. This data is used to learn the normal behavior.


Our algorithm uses this collected data to train models that then map the expected behavior of the machine in various scenarios.


New sensor data is continuously compared with this normal behavior, allowing deviations (so-called “anomalies”) to be identified.


The specially developed “Health Score” of our Digital Twin software shows the health status of the machine in real time so that faults can be detected and rectified at an early stage.


Advantages and opportunities



A digital twin enables specific analyses and insights that would not be possible without it. Instead of rigid maintenance plans, targeted maintenance can be carried out as soon as the machine's behaviour deviates significantly from the learned normal state (the digital twin). This precise fault resolution and maintenance offers numerous advantages - from increased efficiency to a reduction in downtime.



Real-time condition monitoring and predictive maintenance: As already mentioned, the digital twin serves as a basis for comparison with new sensor data. AI-supported systems analyse data deviations for relevance and cause so that precise error messages enable companies to take predictive maintenance measures.


Technologies & implementation challenges


The Digital Twin software integrates technologies such as IoT sensors, AI-supported algorithms and real-time analyses. IoT sensors continuously supply data on the machine, such as temperature, pressure and vibrations. AI algorithms process these data streams, identify patterns and calculate deviations. The sensors record both normal behavior and deviations that may indicate potential problems.

Although the technologies behind digital twin software provide impressive insights and analyses, their implementation poses a number of challenges for companies.

 

  • Data requirements and security: Creating a digital twin requires extensive, digitally recorded sensor data that must be stored and processed securely. Data protection and cyber security play a central role here.

  • Integration into existing systems: In order to benefit from the advantages of digital twin software, the software must be seamlessly integrated into existing systems. This can be a challenge, especially with older machines and systems. aiomatic offers a scalable software solution that fits any IT environment - without the need to install additional hardware.

  • Complexity and cost factors: The introduction of Digital Twin software requires a one-off initial investment and technical expertise. Our experts at aiomatic will be happy to advise you on this.



Future Outlook


In the coming years, technology will continue to develop and gain in importance. Future trends such as the increased integration of AI and automation promise additional efficiency gains. Companies that rely on digital twin software for machine maintenance at an early stage can gain a significant competitive advantage.

Key Learnings and recommendations


Digital Twin software offers companies clear advantages for the monitoring and maintenance of machines and systems. Implementation should be well planned and carried out in collaboration with experts. The behavior of machines in different environments and scenarios must always be kept in mind in order to exploit the full potential of digital technology.



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