This paper casts pipe inspection by ultrasonic guided waves in a feature extraction and automatic classification framework. The specific defect under investigation is a small notch cut in an ASTM-A53-F steel pipe at depths ranging from 1% to 17% of the pipe cross-sectional area. A semi-analytical finite element method is first used to model wave propagation in the pipe. In the experiment, reflection measurements are taken and six features are extracted from the discrete wavelet decomposition of the raw signals and from the Hilbert and Fourier transforms of the reconstructed signals. A six-dimensional damage index is then constructed, and it is fed to an artificial neural network that classifies the size and the location of the notch. Overall, the wavelet-based multifeature analysis demonstrates good classification performance and robustness against noise and changes in some of the operating parameters.
Issue Section:
Research Papers
Keywords:
ultrasonic materials testing,
pipes,
neural nets,
discrete wavelet transforms,
finite element analysis,
feature extraction,
inspection,
steel,
ultrasonic propagation,
Hilbert transforms,
Fourier transforms,
Pipe Inspection,
Guided Waves,
Wavelet Transform,
Feature Extraction,
Neural Networks
Topics:
Artificial neural networks,
Damage,
Inspection,
Pipes,
Signals,
Wavelets,
Waves,
Feature extraction,
Reflection,
Steel
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