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Aims

The ultimate aim is to provide a revolutionary solution for infrastructure condition and structural health monitoring (SHM), which will have considerable impact on a wide range of areas including the rapid inspection of defects in large structures, new materials, ageing infrastructures, safety critical systems for aircraft, power plants, oil/gas/water pipelines, offshore infrastructure, rail tracks etc. Theoretical studies, numerical modelling, and experimental tests will be carried out. Eventually, the project will produce a low-cost, self-powering, electromagnetic sensing and networked monitoring system. The system will be developed by a novel integration of radio frequency identification (RFID), pulsed eddy current (PEC) quantitative non-destructive evaluation (QNDE), and acoustic emission techniques facilitated by powerful nonlinear system identification and analysis based signal processing methods for fault diagnosis and condition/health monitoring. These will, to a great extent, bridge the gap between non-destructive evaluation (NDE) and SHM, resolving a series of long standing problems in this important subject area including monitoring infrastructure for fatigue damage, crack growth, residual/internal stress, corrosion, radiation damage, ageing; life prediction methodologies using in-situ data; and using the permanent low-cost, wireless sensor networks for monitoring dynamic, unpredictable environments.

 

Objectives

This project is a timely effort to bring together the expertise of electromagnetic NDE (ENDE) at Newcastle, the research expertise in nonlinear system identification and analysis at Sheffield and the achievements in software architecture with cloud-based solution at York to accomplish the following objectives:

  • Building interdisciplinary research and expanding current network. The collaborative research will concentrate on the interdisciplinary research by integration of complementary expertise, which is reflected from the exchanges.
  • Applying non-linear system identification and analysis based signal processing to assess the location, size, and microstructure of defects in structures and form further decision makings.
  • Developing self-powering electromagnetic sensor networks to facilitate the application of the new RFID and PEC sensing based technology with power management.
  • Building cloud based solution software architectures, compressed sensing, robust communication and data management
  • Demonstration and evaluation of a low-cost wireless sensor network and intelligent monitoring system for corrosion and stress.

     

       

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