Industrial Asset Management

The industrial asset management R&D has a strong tradition of industrially oriented and applied R&D work on smart industrial assets and complex systems, integrated operations and modern maintenance process, as well as condition monitoring and intelligent maintenance interventions.

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The R&D activities on Industrial Asset Management aim at enabling modern industrial transformation in both offshore and land-based industrial sectors, as well as in the public sector, with a focus on Industry 4.0, digitalisation, sustainability, and green transition to enhance asset integrity, dependability, and resilience.

Reliability, operability, maintainability, and technical & safety integrity characteristics of any industrial asset (i.e. production plants, process facilities, energy production and distribution systems, machines and equipment, infrastructure and civil structures, public transport, maritime, construction, etc.) have direct effects on economics, safety & security, societal impact, as well as sustainability and environmental impact in all industrial sectors. Those issues are even more critical when Industry 4.0, Internet of Things, digitalisation, and green transition change the future of industrial assets and public infrastructures.

The R&D work on Industrial Asset Management mainly cover three main tracks:

Industry and technology concept. INDUSTRY 4.0
Illustration: Shutterstock
  • Smart industrial assets, complex industrial systems, and digital infrastructures
  • Integrated operations, modern maintenance process, and smart asset support capabilities
  • Condition monitoring, predictive analytics, and intelligent maintenance interventions

The R&D work on industrial asset management has a long tradition for practical industrial orientation in R&D activities in close collaboration with various industrial sectors. The relevant sectors include;

  • Oil & gas production, processing, and distribution
  • Offshore wind energy farms
  • Clean energy sources and transmission systems
  • Civil structures and public infrastructures
  • Manufacturing, production and process facilities
  • Smart cities, transport, and logistics
  • Engineering education and innovation

R&D profile

Smart industrial assets, complex industrial systems, and digital infrastructures

R&D activities in this track have a main focus on practical issues and challenges under modern asset digitalisation processes, inter-dependent industrial asset contexts, complex systems approach to modern industrial assets, as well as smart asset management and operational models under uncertain conditions from the perspectives of asset integrity, dependability, resilience, as well as competitive asset performance.

Some selected R&D projects on this track include;

Integrated operations, modern maintenance process, and smart asset support capabilities   

This R&D track perform in-depth studies on practical issues and challenges related to remote operations and decision support, industrial work processes, reliable and safe operations, data-driven operational and maintenance practices, industrial logistics and intralogistics operations with automation and digitalization, as well as organizational issues under complex operational and maintenance conditions related to both conventional assets as well as assets under digital transformation.     

Some selected R&D projects on this track include;

Condition monitoring, predictive analytics, and intelligent maintenance interventions

The R&D work on this track has a main focus on practical issues and challenges in terms of diagnostics and prognostics, reliability, technical and safety integrity, as well as predictive analytics for risk-based and smart maintenance interventions in technical systems, engineering equipment, and components of Industrial assets in various lifecycle phases. This track also explores modern inspection and maintenance practices for life extension, re-use, and recycling purposes.        

Some selected R&D projects on this track include;

Lab facilities

More about the lab

As an integral part of the Master’s programme in Industrial Asset Management, IAMLAB bridges theory and practice by offering a hands-on, research-driven environment for students and researchers.

The lab is designed to equip students with the competencies required for modern asset management, condition monitoring, and predictive maintenance across industrial sectors.

IAMLAB supports experiential learning through advanced physical and digital infrastructure. Students gain direct exposure to diagnostic techniques using a fully equipped fault simulator, capable of replicating real-world faults such as imbalance, misalignment, bent shafts, and bearing or gear defects. Integrated with SKF IMX8 monitoring systems and SKF @ptitude Analyst software, the simulator enables both learning and experimental research in fault detection, diagnosis, and prognosis.

In addition, the lab provides facilities for building and validating Digital Twins, supported by augmentation and virtualization tools. A comprehensive suite of industry-standard software, such as CAMEO Systems Modeller (MBSE), AnyLogic (industrial simulation and prescriptive analytics), Matlab and Azure ML (predictive analytics), Miriam (RAM analytics), and Hexagon (Asset and maintenance management), ensures students develop digital and analytical fluency.

IAMLAB also nurtures innovation through a small-scale Arduino and Azure-based workshop, encouraging students to prototype novel solutions. With its combined physical spaces (KE C-285 and C-289) and digital environments, IAMLAB plays a central role in fostering robust, future-oriented asset management education, training, and practical learning.

Researchers

Professor
51831440
Faculty of Science and Technology
Department of Mechanical and Structural Engineering and Materials Science
Associate Professor
51832794
Faculty of Science and Technology
Department of Mechanical and Structural Engineering and Materials Science
Adjunct Associate Professor
Faculty of Science and Technology
Department of Mechanical and Structural Engineering and Materials Science
Assistant Professor
Faculty of Science and Technology
Department of Mechanical and Structural Engineering and Materials Science
Adjunct Professor
Faculty of Science and Technology
Department of Mechanical and Structural Engineering and Materials Science
PhD Candidate
Faculty of Science and Technology
Department of Mechanical and Structural Engineering and Materials Science

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