Duration: 1-2 years

Abstract:

Asset management across industries such as infrastructure, energy, and manufacturing is undergoing a critical transformation. Current practices are often characterised by manual inspections, fragmented data sources, and reactive maintenance strategies. These limitations lead to inefficiencies, increased operational costs, and unnecessary environmental impact.

Emerging technologies such as artificial intelligence (AI) and extended reality (XR) offer significant potential to address these challenges. However, their combined application in asset management remains largely underexplored. In particular, the integration of AI-enhanced XR for predictive maintenance, real-time monitoring, and lifecycle-oriented decision-making has not yet been systematically realised.

This project addresses this gap by developing a novel, cross-industry approach to AI- and human-centred asset management. It focuses on combining advanced data analytics with immersive technologies to support informed, proactive, and sustainable decision-making. Key elements include integrating AI with human expertise, developing multimodal decision-support systems, and facilitating collaboration and knowledge sharing among stakeholders. The approach is explicitly aligned with long-term sustainability objectives, including resource efficiency and reduced environmental impact.

 

Research Objectives:

The project pursues the following core objectives:

  • Development of AI-enhanced XR systems: Design and implement XR-based platforms integrated with AI-driven analytics to simulate, visualise, and predict asset conditions, enabling proactive and data-informed decision-making.

  • Advancement of predictive maintenance strategies: Apply AI methods to analyse real-time and historical data for failure prediction and maintenance optimisation, reducing downtime and extending asset lifecycles.

  • Resource and impact optimisation: Use XR-based simulations to evaluate and optimise the environmental and economic performance of asset management strategies.

  • Real-time monitoring and visualisation: Develop interactive, immersive environments that provide stakeholders with intuitive access to real-time asset data and system states.

  • Sustainability integration: Ensure that all developed methods and tools contribute to reduced environmental footprints, improved resource efficiency, and more sustainable operational practices.

 

Ziele Grafik

 

Expected Impact:

The project aims to significantly improve the sustainability and efficiency of asset management practices. Enabling predictive and data-driven maintenance reduces resource consumption, lowers emissions, and decreases operational waste.

In addition, integrating XR technologies enhances workforce capabilities by improving training, situational awareness, and decision support. This not only increases operational safety but also strengthens organisational resilience.

Overall, the project positions participating industries at the forefront of digital and sustainable innovation, supporting long-term competitiveness in a rapidly evolving global landscape.

 

Contact Details:

Dr. Sara Scheffer

Email: sara.scheffer@tuwien.ac.at