Brunel Innovation Centre

Brunel Innovation Centre (BIC), established in 2009.

Brunel University London has been highly successful in creating an industrial innovation research resource, namely BIC, that sits between the knowledge base and industry, offering high quality research in an innovative environment and supporting partners in industry to transfer academic research into industrial application. BIC has a wide range of knowledge and expertise in research areas including power ultrasonics, smart non-destructive testing (NDT), active structural health monitoring (SHM) and condition monitoring including hardware and software design, signal and image processing and numerical modelling, sensors, structural integrity assessment for offshore structures, fatigue and fracture analysis, and machine learning.

The centre pursues projects and initiatives that span national and international platforms, including Innovate UK, the European Commission and EPSRC, and has built up a signifcant portfolio of projects in line with its multinational, interdisciplinary vision.

Core areas

Power ultrasonics – detection, software for non-invasive cleaning of fouling, and fouling prevention techniques, anti- or de-icing applications

Smart NDT – autonomous devices, miniaturised, multifunction systems for non-destructive evaluation (NDE) in bespoke environments such as marine, high temperature and aerospace, pattern recognition hardware / software

Active structural health monitoring (SHM) and condition monitoring, and design of associated hardware and software

Signal and image processing and numerical modelling - noise reduction, signal condition and control including active vibration control, data interpretation and analysis

Improved or new and robust sensors such as Piezoelectric or electromagnetic for high temperature inspection, transduction and so on

Electronics – low power consumption for multiple channels, wireless, high speed, wideband and energy harvesting

Structural integrity assessment of offshore facilities

Fatigue and fracture analysis

Machine learning such as decision forest / deep learning models