Defective Areas Identification in Aircraft Components by Bivariate EMD Analysis of Ultrasound Signals

  • Marco Leo
  • David Looney
  • Tiziana D’Orazio
  • Danilo P. Mandic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5998)


In recent years many alternative methodologies and techniques have been proposed to perform non-destructive inspection and maintenance operations of moving structures. In particular, ultrasonic techniques have shown to be very promising for automatic inspection systems. From the literature, it is evident that the neural paradigms are considered, by now, the best choice to automatically classify ultrasound data. At the same time the most appropriate pre-processing technique is still undecided. The aim of this paper is to propose a new and innovative data pre-processing technique that allows the analysis of the ultrasonic data by a complex extension of the Empirical Mode Decomposition (EMD). Experimental tests aiming to detect defective areas in aircraft components are reported and a comparison with classical approaches based on data normalization or wavelet decomposition is also provided.


Discrete Fourier Transform Empirical Mode Decomposition Phase Synchrony Ensemble Empirical Mode Decomposition Ultrasound Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marco Leo
    • 1
  • David Looney
    • 2
  • Tiziana D’Orazio
    • 1
  • Danilo P. Mandic
    • 2
  1. 1.Italian National Research CouncilInstitute of Intelligent Systems for AutomationBariItaly
  2. 2.Department of Electrical and Electronic EngineeringImperial College of Science, Technology and MedicineLondonUK

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