Aging infrastructure presents countless challenges such as inefficiency, escalating maintenance costs, and safety risks, to name a few.

At Asintech, we offer practical solutions through the creation of digital replicas of physical assets, offering a state-of-the-art framework for optimising the management and maintenance of important assets.

What is a Digital Twin?

A digital twin is a dynamic, virtual representation of a physical asset which merge physics-based models with real-time sensor data. This hybrid approach merges first-principle based engineering analysis with the flexibility of data-driven techniques, continually updated with real-time data to provide insights and predictive capabilities to engineering models. This technology goes beyond traditional simulations by enabling intelligent condition monitoring, predictive maintenance, and optimised performance.

Addressing aging infrastructure challenges

Aging infrastructure, particularly in mining, manufacturing, transport, and power generation, faces significant issues in terms of efficiency, reliability, and environmental impact. Digital Twins address these challenges in several ways:

• Predicting failures before they occur, subsequently minimising unplanned outages and extending the lifespan of aging components. For instance, they can intelligently monitor the health of gearbox drivetrains in real-time, guiding operators in maintenance and operational decisions.
• Analysing the effects of system changes. Digital Twins can simulate maintenance activities, upgrades or operational changes. This provides valuable insights, helping to inform decision-making and continuous improvement.
• Evaluating real-time status of assets. Digital Twins enhance transparency, monitoring, and ensure compliance with standards. Operators can see the current condition of their assets at any time even though some aspects are not directly measured, making it easier to manage and report on their status.
• Forecasting long-term effects. Engineers can use Digital Twins to forecast the long-term effects of aging infrastructure. They can experiment with different retrofit options and assess their impacts before implementation. This ensures that chosen solutions will deliver the desired improvements in efficiency, reliability, and safety.
• Modelling Environmental Impact. Digital Twins can model the environmental impact of assets under various operating scenarios, such as emissions, resource consumption, power consumption, and waste generation. This helps ensure compliance with environmental regulations and identifies opportunities to improve sustainability.

Unlocking mechanical engineering insights through hybrid modelling

Asintech specializes in vibration measurement, testing and analysis. Monitoring and assessment rotating machinery vibration response allows early detection of structural issues like misalignments, bearing faults, loose connections, and fatigue cracks.

Integration of measured data with physics-based finite element analysis, coupled with the implementation of algorithms to identify correlations yield insights that are undetectable by manual inspection. This hybrid modelling approach facilitates a deeper understanding of mechanical and structural systems and proactively address potential issues and supports informed decisions on maintenance, upgrades, and operational changes, offering a robust framework for optimizing performance, reliability, and sustainability of aging infrastructure throughout its lifecycle.

Conclusion

Asintech’s expertise in hybrid modelling, which integrates advanced vibration analysis, finite element modelling, and machine learning, enables our clients to uncover critical insights and make informed decisions to extend the lifespan of their infrastructure.

As the demands on aging assets grow, Digital Twins offers a powerful framework for navigating these complexities and ensuring long-term sustainability.