Smart Assets, Digital and Integrated Operations, and Condition monitoring and Maintenance Analytics.
The R&D focus of the Industrial Asset Management group is to enable modern industrial transformation in both offshore and land-based industrial sectors through Industry 4.0, Digitalization, and Green transition to realize industrial assets and systems that are reliable, operable, maintainable, and dependable.
Reliability, operability, maintainability, and integrity characteristics of any industrial asset (i.e. production plants, process facilities, energy production and distribution systems, machines and equipment, infrastructure and civil structures, etc) have large effects on Economics, Safety & Security, as well as Environmental impact in almost all Industrial sectors. Those issues become even more critical when Industry 4.0, Digitalization, and Green transition change the future of Industrial assets.
Our Applied research related to Reliability, Operability, Maintainability, and Integrity issues targets various sectors including Energy production and distribution (Oil & Gas, Offshore wind, New energy sources, etc.), Process industry, Manufacturing and Production, Infrastructure and Civil structures, as well as the Public sector.
Our contribution mainly comes through three R&D tracks:
- Smart Assets and Maintenance Engineering
- Digital and Integrated Operations
- Smart Maintenance: Processes, Systems and Analytics
By doing so, our continuous ambition is to collaborate with Industrial sectors and other R&D partners to jointly develop Assets-driven future solutions to comply with UN Sustainable goals.
Research areas
Research themes
Oil & Gas (Topside and Subsea)
Offshore Wind Farm
Clean Energy
Tunnel & Infrastructure
Smart city assets and production facilities
Engineering, Education and Innovation
UN Sustainable Goals
Lab facilities
IAMLAB, at KE C-285 and C-289, is a cyber-physical lab that:
- Enables students to understand and gain hands-on condition monitoring and diagnostic techniques.
- Provides a physical fault simulator to experimentally research the most common industrial faults (imbalance, misalignment, bent shaft, bearing faults, gear faults) and the ways to detect, diagnose and prognosis such faults. The fault simulator is equipped with SKF IMX8 monitoring systems and SKF @ptitude Analyst.
- Provides physical counterpart and instrumentations to perform Digital Twin building, Augmentation and Virtualisation tasks.
- Provides a digital environment to utilise:
- Model-based systems engineering software using CAMEO Systems modeller
- Industrial simulation models and prescriptive analytics using AnyLogic
- Predictive analytics using Jupyter, Azure ML, Matlab PdM
- RAM analytics software
- Plant Maintenance Management (SAP HAHA)
- Provides a small-scale Arduino and Azure workshop for minor student innovation projects.
Researchers
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