Real-time eddy-current-testing of metallic structures through statistical learning methodology

Abstract : Summary form only given. There is nowadays an increasing attention and request towards the study and development of systems and techniques for increasing the human safety and security in all aspects of the everyday life. In this framework, the possibility to perform a non-invasive inspection of structures or objects by means of non-destructive testing and evaluation (NDT-NDE) technologies is of particular interests in several applicative fields, ranging from civil engineering to biomedicine, up to aeronautic and nuclear industries [1]. One of the main limitations of the current NDT-NDE approaches is the high computational burden that prevents the real-time or almost real-time investigation of the structure under test (SUT). As a matter of fact, these techniques, aimed at minimizing a suitable cost function that quantifies the mismatch between the acquired data and the retrieved/simulated data, are based on iterative deterministic [2] stochastic [3, 4] or also hybrid optimization approaches that are time-consuming.
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Submitted on : Wednesday, September 20, 2017 - 10:42:29 AM
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Giacomo Oliveri, Paolo Rocca, Lorenzo Poli, Anselmi Nicola, Marco Salucci, et al.. Real-time eddy-current-testing of metallic structures through statistical learning methodology. 2016 Progress in Electromagnetic Research Symposium (PIERS), Aug 2016, Shangai, China. pp.3957-3958, ⟨10.1109/PIERS.2016.7735490⟩. ⟨hal-01590754⟩

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