Neural NDT by means of reflected longitudinal and torsional waves modes in long and inaccessible pipes

  • Authors:

  • Barbara Cannas;Francesca Cau;Alessandra Fanni;Augusto Montisci;Pietro Testoni;Mariangela Usai

  • Affiliations:

  • Electrical and Electronic Engineering Department, University of Cagliari, Piazza D'Armi, Italy;Electrical and Electronic Engineering Department, University of Cagliari, Piazza D'Armi, Italy;Electrical and Electronic Engineering Department, University of Cagliari, Piazza D'Armi, Italy;Electrical and Electronic Engineering Department, University of Cagliari, Piazza D'Armi, Italy;Electrical and Electronic Engineering Department, University of Cagliari, Piazza D'Armi, Italy;Electrical and Electronic Engineering Department, University of Cagliari, Piazza D'Armi, Italy

  • Venue:
  • ISTASC'05 Proceedings of the 5th WSEAS/IASME International Conference on Systems Theory and Scientific Computation
  • Year:
  • 2005

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Abstract

The design of Non-Destructive Testing systems for fault detection in long and not accessible pipelines is an actual task in the industrial and civil environment. At this purpose the diagnosis based on the propagation of guided ultrasonic waves along the pipes offers an attractive solution for the fault identification and classification. The authors studied this problem by means of suitable Artificial Neural Network models. Numerical techniques have been used to model different kinds of pipes and faults, and to obtain several returning echoes containing the fault information. Two kinds of excitation waves have been used: longitudinal and torsional wave modes. The obtained signals have been processed in order to filter the noise, to reduce the data dimensionality, and to compute suitable features. The features selected from the signals can be further processed in order to limit the size of the Neural Network models without loss of information. At this purpose, the Principal Component Analysis has been investigated. Finally, the selected features have been used as input for the Neural Network models. In this paper, traditional feed-forward, Multi Layer Perceptron networks have been used to classify position, width, and depth of the defects.