Name
Title | ||
|---|---|---|
Automatic Evaluation Of Flaws In Pipes By Means Of Ultrasonic Waveforms And Neural Networks |
Abstract | ||
|---|---|---|
The ultrasonic inspection technique takes a relevant place in not destructive defect detection. It can be very useful to determine the state of not accessible structure. In this paper a method based on ultrasonic waves inspection to evaluate the dimensions of flaws in not accessible pipes is shown. The method performs the extraction of time and frequency features from simulated ultrasonic waves and the proper reduction of the number of these features. Then a neural network classification evaluates the dimension of the flaws in the pipe under test. The results show low error rates for all classes considered. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IJCNN.2006.246780 | 2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10 |
Keywords | Field | DocType |
neural network,neural nets,error rate,inspection | Ultrasonic testing,Neural network classification,Ultrasonic sensor,Computer vision,Pattern recognition,Computer science,Waveform,Artificial intelligence,Artificial neural network | Conference |
ISSN | Citations | PageRank |
2161-4393 | 1 | 0.43 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giuseppe Acciani | 1 | 111 | 15.41 |
| Gioacchino Brunetti | 2 | 44 | 4.40 |
| Ernesto Chiarantoni | 3 | 23 | 6.34 |
| Girolamo Fornarelli | 4 | 81 | 8.74 |