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F. Cau 1 , A.Fanni, A.Montisci, P.Testoni 2 , M.Usai

TRANSDUCER. PRE-PROCESSOR. FEATURES EXTRACTOR. CLASSIFIER. Excitation Wave. Returning echo. …. x(t). z(t). MLP. y 1. y 2. y 3. A Signal Processing Tool for Non-Destructive-Testing of Inaccessible Pipes. F. Cau 1 , A.Fanni, A.Montisci, P.Testoni 2 , M.Usai.

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F. Cau 1 , A.Fanni, A.Montisci, P.Testoni 2 , M.Usai

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  1. TRANSDUCER PRE-PROCESSOR FEATURES EXTRACTOR CLASSIFIER Excitation Wave Returning echo … x(t) z(t) MLP y1 y2 y3 A Signal Processing Tool for Non-Destructive-Testing of Inaccessible Pipes F.Cau1, A.Fanni, A.Montisci, P.Testoni2, M.Usai DIEE, University of Cagliari 1PhD Student in Industrial Engineering 2Post Doc in Electrical Engineering Abstract • The Non-Destructive Testing (NDT) with ultrasonic guided waves in the pipe wall provides a solution to the fault inspection problem in the industrial and civil plants. • Lamb Waves can be excited at the edge of the pipe and will propagate many meters, returning echoes indicating the presence of faults. • A diagnostic system based on Artificial Neural Networks can recognize and precisely determine the entity of defect. • The signals used to train the classifier of the diagnostic system have been obtained by performing several numerical analyses with finite element commercial codes. Diagnostic System The transducer imposes prescribed circumferential displacements in the accessible pipe section and acquires the reflected waveforms Denoising, data reduction and filter extraction (FFT2 and PCA) A Neural Network MLP makes a decision on the class whose the defect belongs to Data set Features extraction and data reduction Neural Network • 300 numerical simulations of a pipe with different notches in the external wall and one simulation of a fault free pipe (in total 301 simulations). • 36 waveforms for each defect (36 observing points). FFT2, Treshold, PCA • For each of 301 simulation • 460 temporal samples • 36 observing points 17 “features” for each simulation (17x301) • Training set : 17x723 • Validation set: 17x30 • Test set: 17x30 • 1 hidden layer with 20 nodes • 3 Output (angular amplitude, thickness and axial lenght) Test and validation set each of 17x30 New Augmented Training set 17x723 Original 17x241 matrix + 2 new 17x241 matrix perturbed Training set 17x241 Results • Angular Amplitude classification: Mean Error equal to 1.8% • Thickness Classification: Mean Error equal to 2.9% • Width Classification: Mean Error equal to 4.8%

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